Final Year IEEE Project 2013-2014 - Digital Image Processing Project Title and Abstract

Elysium Technologies Private Limited
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ETPL
DIP-001
Local Edge Preserving Multiscale Decomposition For High Dynamic Range Image
Tone Mapping
A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous
filters in its locally adaptive property. The filtered image contains local means everywhere and preserves
local salient edges. Comparisons are made between our filtered result and the results of three other
methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with
this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one
base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer
preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local
window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function
is also proposed for compressing the detail layers. The reproduced image gives a good visualization.
Experimental results on real images demonstrate that our algorithm is especially effective at preserving or
enhancing local details.
ETPL
DIP-002
Multistucture Large Deformation Diffeomorphic Brain Registration( Biomedical
Engineering)
Whole brain MRI registration has many useful applications in group analysis and morphometry, yet
accurate registration across different neuropathological groups remains challenging. Structure-specific
information, or anatomical guidance, can be used to initialize and constrain registration to improve
accuracy and robustness. We describe here a multistructure diffeomorphic registration approach that uses
concurrent subcortical and cortical shape matching to guide the overall registration. Validation
experiments carried out on openly available datasets demonstrate comparable or improved alignment of
subcortical and cortical brain structures over leading brain registration algorithms. We also demonstrate
that a group-wise average atlas built with multistructure registration accounts for greater intersubject
variability and provides more sensitive tensor-based morphometry measurements.
ETPL
DIP-003
Iterative Closest Normal Point for 3D Face Recognition( Pattern Analysis and Machine
Intelligence)
The common approach for 3D face recognition is to register a probe face to each of the gallery faces and
then calculate the sum of the distances between their points. This approach is computationally expensive
and sensitive to facial expression variation. In this paper, we introduce the iterative closest normal point
method for finding the corresponding points between a generic reference face and every input face. The
proposed correspondence finding method samples a set of points for each face, denoted as the closest
normal points. These points are effectively aligned across all faces, enabling effective application of
discriminant analysis methods for 3D face recognition. As a result, the expression variation problem is
addressed by minimizing the within-class variability of the face samples while maximizing the between-
class variability. As an important conclusion, we show that the surface normal vectors of the face at the
sampled points contain more discriminatory information than the coordinates of the points. We have
performed comprehensive experiments on the Face Recognition Grand Challenge database, which is
presently the largest available 3D face database. We have achieved verification rates of 99.6 and 99.2
percent at a false acceptance rate of 0.1 percent for the all versus all and ROC III experiments,
respectively, which, to the best of our knowledge, have seven and four times less error rates, respectively,
compared to the best existing methods on this database.
ETPL
DIP-004
Face Recognition & verification using photometric stergo(Information Forensics and
Security)
This paper presents a new database suitable for both 2-D and 3-D face recognition based on photometric
stereo (PS): the Photoface database. The database was collected using a custom-made four-source PS
device designed to enable data capture with minimal interaction necessary from the subjects. The device,
which automatically detects the presence of a subject using ultrasound, was placed at the entrance to a

busy workplace and captured 1839 sessions of face images with natural pose and expression. This meant
that the acquired data is more realistic for everyday use than existing databases and is, therefore, an
invaluable test bed for state-of-the-art recognition algorithms. The paper also presents experiments of
various face recognition and verification algorithms using the albedo, surface normals, and recovered
depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in
the pipeline of PS (i.e., normal field computation and depth map reconstruction) affect recognition and
verification performance. These experiments help to 1) demonstrate the usefulness of PS, and our device
in particular, for minimal-interaction face recognition, and 2) highlight the optimal reconstruction and
recognition algorithms for use with natural-expression PS data. The database can be downloaded from
http://www.uwe.ac.uk/research/Photoface.
ETPL
DIP-005
Objective Quality Assessment of Tone-Mapped Images
Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR)
images provide practically useful tools for the visualization of HDR images on standard LDR displays.
Different TMOs create different tone-mapped images, and a natural question is which one has the best
quality. Without an appropriate quality measure, different TMOs cannot be compared, and further
improvement is directionless. Subjective rating may be a reliable evaluation method, but it is expensive
and time consuming, and more importantly, is difficult to be embedded into optimization frameworks.
Here we propose an objective quality assessment algorithm for tone-mapped images by combining: 1) a
multiscale signal fidelity measure on the basis of a modified structural similarity index and 2) a
naturalness measure on the basis of intensity statistics of natural images. Validations using independent
subject-rated image databases show good correlations between subjective ranking score and the proposed
tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended applications of
TMQI using two examples - parameter tuning for TMOs and adaptive fusion of multiple tone-mapped
images.
ETPL
DIP-006
Segmentation and Tracing of Single Neurons from 3D Confocal Microscope Images(
Biomedical and Health Informatics)
In order to understand the brain, we need to first understand the morphology of neurons. In the
neurobiology community, there have been recent pushes to analyze both neuron connectivity and the
influence of structure on function. Currently, a technical roadblock that stands in the way of these studies
is the inability to automatically trace neuronal structure from microscopy. On the image processing side,
proposed tracing algorithms face difficulties in low contrast, indistinct boundaries, clutter, and complex
branching structure. To tackle these difficulties, we develop Tree2Tree, a robust automatic neuron
segmentation and morphology generation algorithm. Tree2Tree uses a local medial tree generation
strategy in combination with a global tree linking to build a maximum likelihood global tree. Recasting
the neuron tracing problem in a graph-theoretic context enables Tree2Tree to estimate bifurcations
naturally, which is currently a challenge for current neuron tracing algorithms. Tests on cluttered confocal
microscopy images of Drosophila neurons give results that correspond to ground truth within a margin of
$ pm hbox{2.75}$% normalized mean absolute error.
ETPL
DIP-007
Silhoutte Analysis-Based action recognition via Exploiting Human Poses( Circuits and
Systems for Video Technology)
In this paper, we propose a novel scheme for human action recognition that combines the advantages of
both local and global representations. We explore human silhouettes for human action representation by
taking into account the correlation between sequential poses in an action. A modified bag-of-words
model, named bag of correlated poses, is introduced to encode temporally local features of actions. To

utilize the property of visual word ambiguity, we adopt the soft assignment strategy to reduce the
dimensionality of our model and circumvent the penalty of computational complexity and quantization
error. To compensate for the loss of structural information, we propose an extended motion template, i.e.,
extensions of the motion history image, to capture the holistic structural features. The proposed scheme
takes advantages of local and global features and, therefore, provides a discriminative representation for
human actions. Experimental results prove the viability of the complimentary properties of two
descriptors and the proposed approach outperforms the state-of-the-art methods on the IXMAS action
recognition dataset.
ETPL
DIP-008
Pose-Invariant Face Recognition Using Markov Random Fields
One of the key challenges for current face recognition techniques is how to handle pose variations
between the probe and gallery face images. In this paper, we present a method for reconstructing the
virtual frontal view from a given nonfrontal face image using Markov random fields (MRFs) and an
efficient variant of the belief propagation algorithm. In the proposed approach, the input face image is
divided into a grid of overlapping patches, and a globally optimal set of local warps is estimated to
synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it
with images from a training database of frontal faces. The alignments are performed efficiently in the
Fourier domain using an extension of the Lucas-Kanade algorithm that can handle illumination
variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem
using an MRF. The reconstructed frontal face image can then be used with any face recognition
technique. The two main advantages of our method are that it does not require manually selected facial
landmarks or head pose estimation. In order to improve the performance of our pose normalization
method in face recognition, we also present an algorithm for classifying whether a given face image is at
a frontal or nonfrontal pose. Experimental results on different datasets are presented to demonstrate the
effectiveness of the proposed approach
ETPL
DIP-009
Color Video Denoising Based on Combined Interframe and Intercolor Prediction(
Circuits and Systems for Video Technology)
An advanced color video denoising scheme which we call CIFIC based on combined interframe and
intercolor prediction is proposed in this paper. CIFIC performs the denoising filtering in the RGB color
space, and exploits both the interframe and intercolor correlation in color video signal directly by forming
multiple predictors for each color component using all three color components in the current frame as
well as the motion-compensated neighboring reference frames. The temporal correspondence is
established through the joint-RGB motion estimation (ME) which acquires a single motion trajectory for
the red, green, and blue components. Then the current noisy observation as well as the interframe and
intercolor predictors are combined by a linear minimum mean squared error (LMMSE) filter to obtain the
denoised estimate for every color component. The ill condition in the weight determination of the
LMMSE filter is detected and remedied by gradually removing the “least contributing” predictor.
Furthermore, our previous work on the LMMSE filter applied in the adaptive luminance-chrominance
space (LAYUV for short) is revisited. By reformulating LAYUV and comparing it with CIFIC, we
deduce that LAYUV is a restricted version of CIFIC, and thus CIFIC can theoretically achieve lower
denoising error. Experimental results verify the improvement brought by the joint-RGB ME and the
integration of the intercolor prediction, as well as the superiority of CIFIC over LAYUV. Meanwhile,
when compared with other state-of-the-art algorithms, CIFIC provides competitive performance both in
terms of the color peak signal-to-noise ratio and in perceptual quality.

ETPL
DIP-010
Wang-Landau Monte Carlo-Based Tracking Methods for Abrupt Motions( Pattern
Analysis and Machine Intelligence)
We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo (WLMC) sampling
method for dealing with abrupt motions efficiently. Abrupt motions cause conventional tracking methods
to fail because they violate the motion smoothness constraint. To address this problem, we introduce the
Wang-Landau sampling method and integrate it into a Markov Chain Monte Carlo (MCMC)-based
tracking framework. By employing the novel density-of-states term estimated by the Wang-Landau
sampling method into the acceptance ratio of MCMC, our WLMC-based tracking method alleviates the
motion smoothness constraint and robustly tracks the abrupt motions. Meanwhile, the marginal likelihood
term of the acceptance ratio preserves the accuracy in tracking smooth motions. The method is then
extended to obtain good performance in terms of scalability, even on a high-dimensional state space.
Hence, it covers drastic changes in not only position but also scale of a target. To achieve this, we modify
our method by combining it with the N-fold way algorithm and present the N-Fold Wang-Landau
(NFWL)-based tracking method. The N-fold way algorithm helps estimate the density-of-states with a
smaller number of samples. Experimental results demonstrate that our approach efficiently samples the
states of the target, even in a whole state space, without loss of time, and tracks the target accurately and
robustly when position and scale are changing severely
ETPL
DIP-011
Multi-View ML Object Tracking With Online Learning on Riemannian Manifolds by
Combining Geometric Constraints
This paper addresses issues in object tracking with occlusion scenarios, where multiple uncalibrated
cameras with overlapping fields of view are exploited. We propose a novel method where tracking is first
done independently in each individual view and then tracking results are mapped from different views to
improve the tracking jointly. The proposed tracker uses the assumptions that objects are visible in at least
one view and move uprightly on a common planar ground that may induce a homography relation
between views. A method for online learning of object appearances on Riemannian manifolds is also
introduced. The main novelties of the paper include: 1) define a similarity measure, based on geodesics
between a candidate object and a set of mapped references from multiple views on a Riemannian
manifold; 2) propose multi-view maximum likelihood estimation of object bounding box parameters,
based on Gaussian-distributed geodesics on the manifold; 3) introduce online learning of object
appearances on the manifold, taking into account of possible occlusions; 4) utilize projective
transformations for objects between views, where parameters are estimated from warped vertical axis by
combining planar homography, epipolar geometry, and vertical vanishing point; 5) embed single-view
trackers in a three-layer multi-view tracking scheme. Experiments have been conducted on videos from
multiple uncalibrated cameras, where objects contain long-term partial/full occlusions, or frequent
intersections. Comparisons have been made with three existing methods, where the performance is
evaluated both qualitatively and quantitatively. Results have shown the effectiveness of the proposed
method in terms of robustness against tracking drift caused by occlusions.
ETPL
DIP-012
Multi-Atlas Segmentation with Joint Label Fusion ( Pattern Analysis and Machine
Intelligence)
Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in
biomedical images. In this approach, multiple expert-segmented example images, called atlases, are
registered to a target image, and deformed atlas segmentations are combined using label fusion. Among
the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived
from atlas-target intensity similarity have been particularly successful. However, one limitation of these
strategies is that the weights are computed independently for each atlas, without taking into account the
fact that different atlases may produce similar label errors. To address this limitation, we propose a new
solution for the label fusion problem in which weighted voting is formulated in terms of minimizing the

total expectation of labeling error and in which pairwise dependency between atlases is explicitly
modeled as the joint probability of two atlases making a segmentation error at a voxel. This probability is
approximated using intensity similarity between a pair of atlases and the target image in the neighborhood
of each voxel. We validate our method in two medical image segmentation problems: hippocampus
segmentation and hippocampus subfield segmentation in magnetic resonance (MR) images. For both
problems, we show consistent and significant improvement over label fusion strategies that assign atlas
weights independently.
ETPL
DIP-013
Spatially Coherent Fuzzy Clustering for Accurate and Noise-Robust Image
Segmentation
In this letter, we present a new FCM-based method for spatially coherent and noise-robust image
segmentation. Our contribution is twofold: 1) the spatial information of local image features is integrated
into both the similarity measure and the membership function to compensate for the effect of noise; and
2) an anisotropic neighborhood, based on phase congruency features, is introduced to allow more
accurate segmentation without image smoothing. The segmentation results, for both synthetic and real
images, demonstrate that our method efficiently preserves the homogeneity of the regions and is more
robust to noise than related FCM-based methods.
ETPL
DIP-014
Adaptive Markov Random Fields for Joint Unmixing and Segmentation of
Hyperspectral Images
Abstract: Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of
decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their
corresponding proportions (or abundances). Endmember extraction algorithms can be employed for
recovering the spectral signatures while abundances are estimated using an inversion step. Recent works
have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing.
Markov random fields (MRF) are classically used to model these spatial correlations and partition the
image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites
using similarity regions. These regions are built using a self-complementary area filter that stems from
the morphological theory. This kind of filter divides the original image into flat zones where the
underlying pixels have the same spectral values. Once the MRF has been clearly established, a
hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise
variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate
samples according to the corresponding posterior distribution of the unknown parameters and
hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good
performance of the algorithm.
ETPL
DIP-015
Depth Estimation of Face Images Using the Nonlinear Least-Squares Model
Abstract: In this paper, we propose an efficient algorithm to reconstruct the 3D structure of a human face
from one or more of its 2D images with different poses. In our algorithm, the nonlinear least-squares
model is first employed to estimate the depth values of facial feature points and the pose of the 2D face
image concerned by means of the similarity transform. Furthermore, different optimization schemes are
presented with regard to the accuracy levels and the training time required. Our algorithm also embeds
the symmetrical property of the human face into the optimization procedure, in order to alleviate the
sensitivities arising from changes in pose. In addition, the regularization term, based on linear correlation,
is added in the objective function to improve the estimation accuracy of the 3D structure. Further, a
model-integration method is proposed to improve the depth-estimation accuracy when multiple
nonfrontal-view face images are available. Experimental results on the 2D and 3D databases demonstrate
the feasibility and efficiency of the proposed methods.

ETPL
DIP-016
Local Energy Pattern for Texture Classification Using Self-Adaptive Quantization
Thresholds
Abstract: Local energy pattern, a statistical histogram-based representation, is proposed for texture
classification. First, we use normalized local-oriented energies to generate local feature vectors, which
describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local
feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using
histogram specification, and the quantized local feature vector is transformed to a number by N-nary
coding, which helps to preserve more structure information during vector quantization. Finally, the
frequency histogram is used as the representation feature. The performance is benchmarked by material
categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical
statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern,
completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show
that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive
performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to
dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles
(UCLA) dynamic texture database.
ETPL
DIP-017
Perceptual Quality Metric With Internal Generative Mechanism
Abstract: Objective image quality assessment (IQA) aims to evaluate image quality consistently with
human perception. Most of the existing perceptual IQA metrics cannot accurately represent the
degradations from different types of distortion, e.g., existing structural similarity metrics perform well on
content-dependent distortions while not as well as peak signal-to-noise ratio (PSNR) on content-
independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide
of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual
system actively predicts sensory information and tries to avoid residual uncertainty for image perception
and understanding. Inspired by the IGM theory, we adopt an autoregressive prediction algorithm to
decompose an input scene into two portions, the predicted portion with the predicted visual content and
the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary
visual information, and structural similarity procedures are employed to measure its degradation;
distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed
for it. Finally, according to the noise energy deployment on the two portions, we combine the two
evaluation results to acquire the overall quality score. Experimental results on six publicly available
databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics.
ETPL
DIP-018
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A
Comparative Study
Abstract: Visual attention is a process that enables biological and machine vision systems to select the
most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors
driven by task and 2) bottom-up factors that highlight image regions that are different from their
surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency
has been the subject of numerous research efforts during the past 20 years, with many successful
applications in computer vision and robotics. Available models have been tested with different datasets
(e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores
(e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct
comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art
saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video
datasets, using three evaluation scores. We find that although model rankings vary, some models
consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased,

which influences some of the evaluation scores. Computational complexity analysis shows that some
models are very fast, yet yield competitive eye movement prediction accuracy. Different models often
have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye
movement datasets, and evaluation scores are discussed and insights for future work are provided. Our
study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a
unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the
object recognition and detection domains.
ETPL
DIP-019
Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image
Tone Mapping
Abstract: A novel filter is proposed for edge-preserving decomposition of an image. It is different from
previous filters in its locally adaptive property. The filtered image contains local means everywhere and
preserves local salient edges. Comparisons are made between our filtered result and the results of three
other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition
with this filter is proposed for manipulating a high dynamic range image, which has three detail layers
and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base
layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a
local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective
function is also proposed for compressing the detail layers. The reproduced image gives a good
visualization. Experimental results on real images demonstrate that our algorithm is especially effective at
preserving or enhancing local details.
ETPL
DIP-020
LLSURE: Local Linear SURE-Based Edge-Preserving Image Filtering
Abstract: In this paper, we propose a novel approach for performing high-quality edge-preserving image
filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an
estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter
which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast
and exact linear-time algorithm whose computational complexity is independent of the filtering kernel
size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the
effectiveness of the new filter for various computer vision applications, including noise reduction, detail
smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.
ETPL
DIP-021
Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian
Noise
Abstract: Many digital imaging devices operate by successive photon-to-electron, electron-to-voltage,
and voltage-to-digit conversions. These processes are subject to various signal-dependent errors, which
are typically modeled as Poisson-Gaussian noise. The removal of such noise can be effected indirectly by
applying a variance-stabilizing transformation (VST) to the noisy data, denoising the stabilized data with
a Gaussian denoising algorithm, and finally applying an inverse VST to the denoised data. The
generalized Anscombe transformation (GAT) is often used for variance stabilization, but its unbiased
inverse transformation has not been rigorously studied in the past. We introduce the exact unbiased
inverse of the GAT and show that it plays an integral part in ensuring accurate denoising results. We
demonstrate that this exact inverse leads to state-of-the-art results without any notable increase in the
computational complexity compared to the other inverses. We also show that this inverse is optimal in the
sense that it can be interpreted as a maximum likelihood inverse. Moreover, we thoroughly analyze the
behavior of the proposed inverse, which also enables us to derive a closed-form approximation for it. This
paper generalizes our work on the exact unbiased inverse of the Anscombe transformation, which we
have presented earlier for the removal of pure Poisson noise.

ETPL
DIP-022
Blind Separation of Time/Position Varying Mixtures
Abstract: We address the challenging open problem of blindly separating time/position varying mixtures,
and attempt to separate the sources from such mixtures without having prior information about the
sources or the mixing system. Unlike studies concerning instantaneous or convolutive mixtures, we
assume that the mixing system (medium) is varying in time/position. Attempts to solve this problem have
mostly utilized, so far, online algorithms based on tracking the mixing system by methods previously
developed for the instantaneous or convolutive mixtures. In contrast with these attempts, we develop a
unified approach in the form of staged sparse component analysis (SSCA). Accordingly, we assume that
the sources are either sparse or can be “sparsified.” In the first stage, we estimate the filters of the mixing
system, based on the scatter plot of the sparse mixtures' data, using a proper clustering and curve/surface
fitting. In the second stage, the mixing system is inverted, yielding the estimated sources. We use the
SSCA approach for solving three types of mixtures: time/position varying instantaneous mixtures, single-
path mixtures, and multipath mixtures. Real-life scenarios and simulated mixtures are used to
demonstrate the performance of our approach.
ETPL
DIP-023
Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction
Abstract: We present an extension of the BM3D filter to volumetric data. The proposed algorithm,
BM4D, implements the grouping and collaborative filtering paradigm, where mutually similar d -
dimensional patches are stacked together in a (d+1) -dimensional array and jointly filtered in transform
domain. While in BM3D the basic data patches are blocks of pixels, in BM4D we utilize cubes of voxels,
which are stacked into a 4-D “group.” The 4-D transform applied on the group simultaneously exploits
the local correlation present among voxels in each cube and the nonlocal correlation between the
corresponding voxels of different cubes. Thus, the spectrum of the group is highly sparse, leading to very
effective separation of signal and noise through coefficient shrinkage. After inverse transformation, we
obtain estimates of each grouped cube, which are then adaptively aggregated at their original locations.
We evaluate the algorithm on denoising of volumetric data corrupted by Gaussian and Rician noise, as
well as on reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete
Fourier-domain (k-space) measurements. Experimental results demonstrate the state-of-the-art denoising
performance of BM4D, and its effectiveness when exploited as a regularizer in volumetric data
reconstruction.
ETPL
DIP-024
Huber Fractal Image Coding Based on a Fitting Plane
Abstract: Recently, there has been significant interest in robust fractal image coding for the purpose of
robustness against outliers. However, the known robust fractal coding methods (HFIC and LAD-FIC,
etc.) are not optimal, since, besides the high computational cost, they use the corrupted domain block as
the independent variable in the robust regression model, which may adversely affect the robust estimator
to calculate the fractal parameters (depending on the noise level). This paper presents a Huber fitting
plane-based fractal image coding (HFPFIC) method. This method builds Huber fitting planes (HFPs) for
the domain and range blocks, respectively, ensuring the use of an uncorrupted independent variable in the
robust model. On this basis, a new matching error function is introduced to robustly evaluate the best
scaling factor. Meanwhile, a median absolute deviation (MAD) about the median decomposition criterion
is proposed to achieve fast adaptive quadtree partitioning for the image corrupted by salt & pepper noise.
In order to reduce computational cost, the no-search method is applied to speedup the encoding process.
Experimental results show that the proposed HFPFIC can yield superior performance over conventional
robust fractal image coding methods in encoding speed and the quality of the restored image.
Furthermore, the no-search method can significantly reduce encoding time and achieve less than 2.0 s for

the HFPFIC with acceptable image quality degradation. In addition, we show that, combined with the
MAD decomposition scheme, the HFP technique used as a robust method can further reduce the encoding
time while maintaining image quality.
ETPL
DIP-025
Demosaicking of Noisy Bayer-Sampled Color Images With Least-Squares Luma-
Chroma Demultiplexing and Noise Level Estimation
Abstract: This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking
method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-
balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green,
and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of
configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-
adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental
results demonstrate state-of-the-art performance over a wide range of noise levels, with low
computational complexity. Many results with several algorithms, noise levels, and images are presented
on our companion web site along with software to allow reproduction of our results.
ETPL
DIP-026
Multiscale Gradients-Based Color Filter Array Interpolation
Abstract: Single sensor digital cameras use color filter arrays to capture a subset of the color data at each
pixel coordinate. Demosaicing or color filter array (CFA) interpolation is the process of estimating the
missing color samples to reconstruct a full color image. In this paper, we propose a demosaicing method
that uses multiscale color gradients to adaptively combine color difference estimates from different
directions. The proposed solution does not require any thresholds since it does not make any hard
decisions, and it is noniterative. Although most suitable for the Bayer CFA pattern, the method can be
extended to other mosaic patterns. To demonstrate this, we describe its application to the Lukac CFA
pattern. Experimental results show that it outperforms other available demosaicing methods by a clear
margin in terms of CPSNR and S-CIELAB measures for both mosaic patterns.
ETPL
DIP-027
Optimal local dimming for LC image formation with controllable backlighting
Abstract: Light emitting diode (LED)-backlit liquid crystal displays (LCDs) hold the promise of
improving image quality while reducing the energy consumption with signal-dependent local dimming.
However, most existing local dimming algorithms are mostly motivated by simple implementation, and
they often lack concern for visual quality. To fully realize the potential of LED-backlit LCDs and reduce
the artifacts that often occur in current systems, we propose a novel local dimming technique that can
achieve the theoretical highest fidelity of intensity reproduction in either l1 or l2 metrics. Both the exact
and fast approximate versions of the optimal local dimming algorithm are proposed. Simulation results
demonstrate superior performances of the proposed algorithm in terms of visual quality and power
consumption.
ETPL
DIP-028
Multiscale Bi-Gaussian Filter for Adjacent Curvilinear Structures Detection With
Application to Vasculature Images
Abstract: The intensity or gray-level derivatives have been widely used in image segmentation and
enhancement. Conventional derivative filters often suffer from an undesired merging of adjacent objects
because of their intrinsic usage of an inappropriately broad Gaussian kernel; as a result, neighboring
structures cannot be properly resolved. To avoid this problem, we propose to replace the low-level
Gaussian kernel with a bi-Gaussian function, which allows independent selection of scales in the
foreground and background. By selecting a narrow neighborhood for the background with regard to the
foreground, the proposed method will reduce interference from adjacent objects simultaneously
preserving the ability of intraregion smoothing. Our idea is inspired by a comparative analysis of existing
line filters, in which several traditional methods, including the vesselness, gradient flux, and medialness

models, are integrated into a uniform framework. The comparison subsequently aids in understanding the
principles of different filtering kernels, which is also a contribution of this paper. Based on some
axiomatic scale-space assumptions, the full representation of our bi-Gaussian kernel is deduced. The
popular γ-normalization scheme for multiscale integration is extended to the bi-Gaussian operators.
Finally, combined with a parameter-free shape estimation scheme, a derivative filter is developed for the
typical applications of curvilinear structure detection and vasculature image enhancement. It is verified in
experiments using synthetic and real data that the proposed method outperforms several conventional
filters in separating closely located objects and being robust to noise.
ETPL
DIP-029
Visually Lossless Encoding for JPEG2000
Abstract: Due to exponential growth in image sizes, visually lossless coding is increasingly being
considered as an alternative to numerically lossless coding, which has limited compression ratios. This
paper presents a method of encoding color images in a visually lossless manner using JPEG2000. In order
to hide coding artifacts caused by quantization, visibility thresholds (VTs) are measured and used for
quantization of subband signals in JPEG2000. The VTs are experimentally determined from statistically
modeled quantization distortion, which is based on the distribution of wavelet coefficients and the dead-
zone quantizer of JPEG2000. The resulting VTs are adjusted for locally changing backgrounds through a
visual masking model, and then used to determine the minimum number of coding passes to be included
in the final codestream for visually lossless quality under the desired viewing conditions. Codestreams
produced by this scheme are fully JPEG2000 Part-I compliant.
ETPL
DIP-030
Rate-Distortion Analysis of Dead-Zone Plus Uniform Threshold Scalar Quantization
and Its Application—Part I: Fundamental Theory,
Abstract: This paper provides a systematic rate-distortion (R-D) analysis of the dead-zone plus uniform
threshold scalar quantization (DZ+UTSQ) with nearly uniform reconstruction quantization (NURQ) for
generalized Gaussian distribution (GGD), which consists of two aspects: R-D performance analysis and
R-D modeling. In R-D performance analysis, we first derive the preliminary constraint of optimum
entropy-constrained DZ+UTSQ/NURQ for GGD, under which the property of the GGD distortion-rate
(D-R) function is elucidated. Then for the GGD source of actual transform coefficients, the refined
constraint and precise conditions of optimum DZ+UTSQ/NURQ are rigorously deduced in the real
coding bit rate range, and efficient DZ+UTSQ/NURQ design criteria are proposed to reasonably simplify
the utilization of effective quantizers in practice. In R-D modeling, inspired by R-D performance analysis,
the D-R function is first developed, followed by the novel rate-quantization (R-Q) and distortion-
quantization (D-Q) models derived using analytical and heuristic methods. The D-R, R-Q, and D-Q
models form the source model describing the relationship between the rate, distortion, and quantization
steps. One application of the proposed source model is the effective two-pass VBR coding algorithm
design on an encoder of H.264/AVC reference software, which achieves constant video quality and
desirable rate control accuracy.
ETPL
DIP-031
Rate-Distortion Analysis of Dead-Zone Plus Uniform Threshold Scalar Quantization
and Its Application—Part II: Two-Pass VBR Coding for H.264/AVC
Abstract: In the first part of this paper, we derive a source model describing the relationship between the
rate, distortion, and quantization steps of the dead-zone plus uniform threshold scalar quantizers with
nearly uniform reconstruction quantizers for generalized Gaussian distribution. This source model
consists of rate-quantization, distortion-quantization (D-Q), and distortion-rate (D-R) models. In this part,
we first rigorously confirm the accuracy of the proposed source model by comparing the calculated
results with the coding data of JM 16.0. Efficient parameter estimation strategies are then developed to
better employ this source model in our two-pass rate control method for H.264 variable bit rate coding.
Based on our D-Q and D-R models, the proposed method is of high stability, low complexity and is easy

to implement. Extensive experiments demonstrate that the proposed method achieves: 1) average peak
signal-to-noise ratio variance of only 0.0658 dB, compared to 1.8758 dB of JM 16.0's method, with an
average rate control error of 1.95% and 2) significant improvement in smoothing the video quality
compared with the latest two-pass rate control method.
ETPL
DIP-032
Nonrigid Image Registration With Crystal Dislocation Energy
Abstract: The goal of nonrigid image registration is to find a suitable transformation such that the
transformed moving image becomes similar to the reference image. The image registration problem can
also be treated as an optimization problem, which tries to minimize an objective energy function that
measures the differences between two involved images. In this paper, we consider image matching as the
process of aligning object boundaries in two different images. The registration energy function can be
defined based on the total energy associated with the object boundaries. The optimal transformation is
obtained by finding the equilibrium state when the total energy is minimized, which indicates the object
boundaries find their correspondences and stop deforming. We make an analogy between the above
processes with the dislocation system in physics. The object boundaries are viewed as dislocations (line
defects) in crystal. Then the well-developed dislocation energy is used to derive the energy assigned to
object boundaries in images. The newly derived registration energy function takes the global gradient
information of the entire image into consideration, and produces an orientation-dependent and long-range
interaction between two images to drive the registration process. This property of interaction endows the
new registration framework with both fast convergence rate and high registration accuracy. Moreover, the
new energy function can be adapted to realize symmetric diffeomorphic transformation so as to ensure
one-to-one matching between subjects. In this paper, the superiority of the new method is theoretically
proven, experimentally tested and compared with the state-of-the-art SyN method. Experimental results
with 3-D magnetic resonance brain images demonstrate that the proposed method outperforms the
compared methods in terms of both registration accuracy and computation time.
ETPL
DIP-033
Double Shrinking Sparse Dimension Reduction
Abstract: Learning tasks such as classification and clustering usually perform better and cost less (time
and space) on compressed representations than on the original data. Previous works mainly compress data
via dimension reduction. In this paper, we propose “double shrinking” to compress image data on both
dimensionality and cardinality via building either sparse low-dimensional representations or a sparse
projection matrix for dimension reduction. We formulate a double shrinking model (DSM) as
an l1 regularized variance maximization with constraint ||x||2=1, and develop a double shrinking algorithm
(DSA) to optimize DSM. DSA is a path-following algorithm that can build the whole solution path of
locally optimal solutions of different sparse levels. Each solution on the path is a “warm start” for
searching the next sparser one. In each iteration of DSA, the direction, the step size, and the Lagrangian
multiplier are deduced from the Karush-Kuhn-Tucker conditions. The magnitudes of trivial variables are
shrunk and the importances of critical variables are simultaneously augmented along the selected
direction with the determined step length. Double shrinking can be applied to manifold learning and
feature selections for better interpretation of features, and can be combined with classification and
clustering to boost their performance. The experimental results suggest that double shrinking produces
efficient and effective data compression.
ETPL
DIP-034
Reinitialization-Free Level Set Evolution via Reaction Diffusion
Abstract: This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is
completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is
introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be

derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step
splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then
solve the diffusion equation. The second step regularizes the level set function obtained in the first step to
ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from
LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple
finite difference method, which is very easy to implement. The proposed RD method can be generalized
to solve the LSE for both variational level set method and partial differential equation-based level set
method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and
promising experimental results on synthetic and real images validate the effectiveness of the proposed
RD-LSE approach.
ETPL
DIP-035
Track Creation and Deletion Framework for Long-Term Online Multiface Tracking
Abstract: To improve visual tracking, a large number of papers study more powerful features, or better
cue fusion mechanisms, such as adaptation or contextual models. A complementary approach consists of
improving the track management, that is, deciding when to add a target or stop its tracking, for example,
in case of failure. This is an essential component for effective multiobject tracking applications, and is
often not trivial. Deciding whether or not to stop a track is a compromise between avoiding erroneous
early stopping while tracking is fine, and erroneous continuation of tracking when there is an actual
failure. This decision process, very rarely addressed in the literature, is difficult due to object detector
deficiencies or observation models that are insufficient to describe the full variability of tracked objects
and deliver reliable likelihood (tracking) information. This paper addresses the track management issue
and presents a real-time online multiface tracking algorithm that effectively deals with the above
difficulties. The tracking itself is formulated in a multiobject state-space Bayesian filtering framework
solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step
decides when to add or remove a target from the tracker, where decisions rely on multiple cues such as
face detections, likelihood measures, long-term observations, and track state characteristics. The method
has been applied to three challenging data sets of more than 9 h in total, and demonstrate a significant
performance increase compared to more traditional approaches (Markov Chain Monte Carlo, reversible-
jump Markov Chain Monte Carlo) only relying on head detection and likelihood for track management.
ETPL
DIP-036
Wavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification
Abstract: In this paper, we propose a new texture descriptor for both static and dynamic textures. The new
descriptor is built on the wavelet-based spatial-frequency analysis of two complementary wavelet
pyramids: standard multiscale and wavelet leader. These wavelet pyramids essentially capture the local
texture responses in multiple high-pass channels in a multiscale and multiorientation fashion, in which
there exists a strong power-law relationship for natural images. Such a power-law relationship is
characterized by the so-called multifractal analysis. In addition, two more techniques, scale normalization
and multiorientation image averaging, are introduced to further improve the robustness of the proposed
descriptor. Combining these techniques, the proposed descriptor enjoys both high discriminative power
and robustness against many environmental changes. We apply the descriptor for classifying both static
and dynamic textures. Our method has demonstrated excellent performance in comparison with the state-
of-the-art approaches in several public benchmark datasets.
ETPL
DIP-037
Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov
Random Fields
Abstract: Despite the recent progress in both pixel-domain and compressed-domain video object tracking,
the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists.
This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences

using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates
the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method
works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from
the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block
motion approximation and global motion compensation. At each frame, the decision of whether a
particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is
updated from frame to frame in order to follow the changes in the object's motion. The proposed method
is tested on a number of standard sequences, and the results demonstrate its advantages over some of the
recent state-of-the-art methods.
ETPL
DIP-038
Online Object Tracking With Sparse Prototypes
Abstract: Online object tracking is a challenging problem as it entails learning an effective model to
account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel
online object tracking algorithm with sparse prototypes, which exploits both classic principal component
analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance
models. We introduce l1regularization into the PCA reconstruction, and develop a novel algorithm to
represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects
are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we
present a method that takes occlusion and motion blur into account rather than simply includes image
observations for model update. Both qualitative and quantitative evaluations on challenging image
sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-
the-art methods.
ETPL
DIP-039
Automatic Dynamic Texture Segmentation Using Local Descriptors and Optical Flow
bstract: A dynamic texture (DT) is an extension of the texture to the temporal domain. How to segment a
DT is a challenging problem. In this paper, we address the problem of segmenting a DT into disjoint
regions. A DT might be different from its spatial mode (i.e., appearance) and/or temporal mode (i.e.,
motion field). To this end, we develop a framework based on the appearance and motion modes. For the
appearance mode, we use a new local spatial texture descriptor to describe the spatial mode of the DT; for
the motion mode, we use the optical flow and the local temporal texture descriptor to represent the
temporal variations of the DT. In addition, for the optical flow, we use the histogram of oriented optical
flow (HOOF) to organize them. To compute the distance between two HOOFs, we develop a simple
effective and efficient distance measure based on Weber's law. Furthermore, we also address the problem
of threshold selection by proposing a method for determining thresholds for the segmentation method by
an offline supervised statistical learning. The experimental results show that our method provides very
good segmentation results compared to the state-of-the-art methods in segmenting regions that differ in
their dynamics.
ETPL
DIP-040
Efficient Image Classification via Multiple Rank Regression
bstract: The problem of image classification has aroused considerable research interest in the field of
image processing. Traditional methods often convert an image to a vector and then use a vector-based
classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is
proposed. Unlike traditional vector-based methods, we employ multiple-rank left projecting vectors and
right projecting vectors to regress each matrix data set to its label for each category. The convergence
behavior, initialization, computational complexity, and parameter determination are also analyzed.
Compared with vector-based regression methods, MRR achieves higher accuracy and has lower
computational complexity. Compared with traditional supervised tensor-based methods, MRR performs

better for matrix data classification. Promising experimental results on face, object, and hand-written digit
image classification tasks are provided to show the effectiveness of our method.
ETPL
DIP-041
Regularized Discriminative Spectral Regression Method for Heterogeneous Face
Matching
Abstract: Face recognition is confronted with situations in which face images are captured in various
modalities, such as the visual modality, the near infrared modality, and the sketch modality. This is
known as heterogeneous face recognition. To solve this problem, we propose a new method called
discriminative spectral regression (DSR). The DSR maps heterogeneous face images into a common
discriminative subspace in which robust classification can be achieved. In the proposed method, the
subspace learning problem is transformed into a least squares problem. Different mappings should map
heterogeneous images from the same class close to each other, while images from different classes should
be separated as far as possible. To realize this, we introduce two novel regularization terms, which reflect
the category relationships among data, into the least squares approach. Experiments conducted on two
heterogeneous face databases validate the superiority of the proposed method over the previous methods.
ETPL
DIP-042
Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search
Abstract: Due to the popularity of social media websites, extensive research efforts have been dedicated
to tag-based social image search. Both visual information and tags have been investigated in the research
field. However, most existing methods use tags and visual characteristics either separately or sequentially
in order to estimate the relevance of images. In this paper, we propose an approach that simultaneously
utilizes both visual and textual information to estimate the relevance of user tagged images. The
relevance estimation is determined with a hypergraph learning approach. In this method, a social image
hypergraph is constructed, where vertices represent images and hyperedges represent visual or textual
terms. Learning is achieved with use of a set of pseudo-positive images, where the weights of hyperedges
are updated throughout the learning process. In this way, the impact of different tags and visual words can
be automatically modulated. Comparative results of the experiments conducted on a dataset including
370+images are presented, which demonstrate the effectiveness of the proposed approach.
ETPL
DIP-043
Action Search by Example Using Randomized Visual Vocabularies
Abstract: Because actions can be small video objects, it is a challenging problem to search for similar
actions in crowded and dynamic scenes when a single query example is provided. We propose a fast
action search method that can efficiently locate similar actions spatiotemporally. Both the query action
and the video datasets are characterized by spatio-temporal interest points. Instead of using a unified
visual vocabulary to index all interest points in the database, we propose randomized visual vocabularies
to enable fast and robust interest point matching. To accelerate action localization, we have developed a
coarse-to-fine video subvolume search scheme, which is several orders of magnitude faster than the
existing spatio-temporal branch and bound search. Our experiments on cross-dataset action search show
promising results when compared with the state of the arts. Additional experiments on a 5-h versatile
video dataset validate the efficiency of our method, where an action search can be finished in just 37.6 s
on a regular desktop machine.
ETPL
DIP-044
Robust Albedo Estimation From a Facial Image With Cast Shadow Under General
Unknown Lighting
Abstract: Albedo estimation from a facial image is crucial for various computer vision tasks, such as 3-D
morphable-model fitting, shape recovery, and illumination-invariant face recognition, but the currently
available methods do not give good estimation results. Most methods ignore the influence of cast
shadows and require a statistical model to obtain facial albedo. This paper describes a method for albedo
estimation that makes combined use of image intensity and facial depth information for an image with

cast shadows and general unknown light. In order to estimate the albedo map of a face, we formulate the
albedo estimation problem as a linear programming problem that minimizes intensity error under the
assumption that the surface of the face has constant albedo. Since the solution thus obtained has
significant errors in certain parts of the facial image, the albedo estimate needs to be compensated. We
minimize the mean square error of albedo under the assumption that the surface normals, which are
calculated from the facial depth information, are corrupted with noise. The proposed method is simple
and the experimental results show that this method gives better estimates than other methods.
ETPL
DIP-045
Separable Markov Random Field Model and Its Applications in Low Level Vision
Abstract: This brief proposes a continuously-valued Markov random field (MRF) model with separable
filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF
modeling. In this framework, we design a novel gradient-based discriminative learning method to learn
the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable
filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By
implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and
fast image demosaicing with high-quality results.
ETPL
DIP-046
Two-Direction Nonlocal Model for Image Denoising
Abstract: Similarities inherent in natural images have been widely exploited for image denoising and
other applications. In fact, if a cluster of similar image patches is rearranged into a matrix, similarities
exist both between columns and rows. Using the similarities, we present a two-directional nonlocal
(TDNL) variational model for image denoising. The solution of our model consists of three components:
one component is a scaled version of the original observed image and the other two components are
obtained by utilizing the similarities. Specifically, by using the similarity between columns, we get a
nonlocal-means-like estimation of the patch with consideration to all similar patches, while the weights
are not the pairwise similarities but a set of clusterwise coefficients. Moreover, by using the similarity
between rows, we also get nonlocal-autoregression-like estimations for the center pixels of the similar
patches. The TDNL model leads to an alternative minimization algorithm. Experiments indicate that the
model can perform on par with or better than the state-of-the-art denoising methods.
ETPL
DIP-047
Optimizing the Error Diffusion Filter for Blue Noise Halftoning With Multiscale Error
Diffusion
Abstract: A good halftoning output should bear a blue noise characteristic contributed by isotropically-
distributed isolated dots. Multiscale error diffusion (MED) algorithms try to achieve this by exploiting
radially symmetric and noncausal error diffusion filters to guarantee spatial homogeneity. In this brief, an
optimized diffusion filter is suggested to make the diffusion close to isotropic. When it is used with MED,
the resulting output has a nearly ideal blue noise characteristic.
ETPL
DIP-049
Sparse Representation With Kernels
Abstract: Recent research has shown the initial success of sparse coding (Sc) in solving many computer
vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which
helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation
(KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an
implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and
kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching
(SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover,

KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of
Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can
be considered a soft assignment extension of HIK-based feature quantization in the feature coding
process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative
sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to
kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel
matrix approximation, especially when a small fraction of the data is used. Extensive experimental results
demonstrate promising results of KSR in image classification, face recognition, and kernel matrix
approximation. All these applications prove the effectiveness of KSR in computer vision and machine
learning tasks.
ETPL
DIP-050
Image-Difference Prediction: From Grayscale to Color
Abstract: Existing image-difference measures show excellent accuracy in predicting distortions, such as
lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one
example of this is gamut mapping. This is partly because they either do not interpret chromatic
information correctly or they ignore it entirely. We present an image-difference framework that
comprises image normalization, feature extraction, and feature combination. Based on this framework,
we create image-difference measures by selecting specific implementations for each of the steps.
Particular emphasis is placed on using color information to improve the assessment of gamut-mapped
images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-
mapping dataset than all other evaluated measures.
ETPL
DIP-051
When Does Computational Imaging Improve Performance?
Abstract: A number of computational imaging techniques are introduced to improve image quality by
increasing light throughput. These techniques use optical coding to measure a stronger signal level.
However, the performance of these techniques is limited by the decoding step, which amplifies noise.
Although it is well understood that optical coding can increase performance at low light levels, little is
known about the quantitative performance advantage of computational imaging in general settings. In this
paper, we derive the performance bounds for various computational imaging techniques. We then discuss
the implications of these bounds for several real-world scenarios (e.g., illumination conditions, scene
properties, and sensor noise characteristics). Our results show that computational imaging techniques do
not provide a significant performance advantage when imaging with illumination that is brighter than
typical daylight. These results can be readily used by practitioners to design the most suitable imaging
systems given the application at hand.
ETPL
DIP-052
Anisotropic Interpolation of Sparse Generalized Image Samples
Abstract: Practical image-acquisition systems are often modeled as a continuous-domain prefilter
followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse
response of the device. In this paper, our goal is to interpolate images from a given subset of such
samples. We express our solution in the continuous domain, considering consistent resampling as a data-
fidelity constraint. To make the problem well posed and ensure edge-preserving solutions, we develop an
efficient anisotropic regularization approach that is based on an improved version of the edge-enhancing
anisotropic diffusion equation. Following variational principles, our reconstruction algorithm minimizes
successive quadratic cost functionals. To ensure fast convergence, we solve the corresponding sequence
of linear problems by using multigrid iterations that are specifically tailored to their sparse structure. We
conduct illustrative experiments and discuss the potential of our approach both in terms of algorithmic

design and reconstruction quality. In particular, we present results that use as little as 2% of the image
samples.
ETPL
DIP-053
Clustered-Dot Halftoning With Direct Binary Search
Abstract: In this paper, we present a new algorithm for aperiodic clustered-dot halftoning based on direct
binary search (DBS). The DBS optimization framework has been modified for designing clustered-dot
texture, by using filters with different sizes in the initialization and update steps of the algorithm.
Following an intuitive explanation of how the clustered-dot texture results from this modified framework,
we derive a closed-form cost metric which, when minimized, equivalently generates stochastic clustered-
dot texture. An analysis of the cost metric and its influence on the texture quality is presented, which is
followed by a modification to the cost metric to reduce computational cost and to make it more suitable
for screen design.
ETPL
DIP-054
Task-Specific Image Partitioning
Abstract: Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms
used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard
to the task in hand. We propose a task-specific image partitioning framework to produce a region-based
image representation that will lead to a higher task performance than that reached using any task-
oblivious partitioning framework and existing supervised partitioning framework, albeit few in number.
The proposed method partitions the image by means of correlation clustering, maximizing a linear
discriminant function defined over a superpixel graph. The parameters of the discriminant function that
define task-specific similarity/dissimilarity among superpixels are estimated based on structured support
vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better
generalization ability while the construction of the superpixel graph used to define the discriminant
function allows a rich set of features to be incorporated to improve discriminability and robustness. We
evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that
task-aware partitioning leads to better labeling performance than the partitioning computed by the state-
of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image
partitioning paradigm is widely applicable to improving performance in high-level image understanding
tasks
ETPL
DIP-055
Generalized Inverse-Approach Model for Spectral-Signal Recovery
Abstract: We have studied the transformation system of a spectral signal to the response of the system as
a linear mapping from higher to lower dimensional space in order to look more closely at inverse-
approach models. The problem of spectral-signal recovery from the response of a transformation system
is generally stated on the basis of the generalized inverse-approach theorem, which provides a modular
model for generating a spectral signal from a given response value. The controlling criteria, including the
robustness of the inverse model to perturbations of the response caused by noise, and the condition
number for matrix inversion, are proposed, together with the mean square error, so as to create an
efficient model for spectral-signal recovery. The spectral-reflectance recovery and color correction of
natural surface color are numerically investigated to appraise different illuminant-observer transformation
matrices based on the proposed controlling criteria both in the absence and the presence of noise.
ETPL
DIP-056
Spatio-Temporal Auxiliary Particle Filtering With -Norm-Based Appearance Model
Learning for Robust Visual Tracking
Abstract: In this paper, we propose an efficient and accurate visual tracker equipped with a new particle
filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker
avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-

known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle
filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering
algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully
implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP)
equipped with l1-norm optimization has been utilized to obtain a new appearance model learning block
for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework
based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed
spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using
challenging video sequences, and the results confirm the advantage of using this tracker.
ETPL
DIP-057
Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image
Classification
Abstract: It is a significant challenge to classify images with multiple labels by using only a small number
of labeled samples. One option is to learn a binary classifier for each label and use manifold
regularization to improve the classification performance by exploring the underlying geometric structure
of the data distribution. However, such an approach does not perform well in practice when images from
multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is
insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask
learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple
classification tasks by exploiting the common structure of these tasks. It effectively controls the model
complexity because different tasks limit one another's search volume, and the manifold regularization
ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct
extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38
classes, by comparing MRMTL with popular image classification algorithms. The results suggest that
MRMTL is effective for image classification.
ETPL
DIP-058
Linear Distance Coding for Image Classification
Abstract: The feature coding-pooling framework is shown to perform well in image classification tasks,
because it can generate discriminative and robust image representations. The unavoidable information
loss incurred by feature quantization in the coding process and the undesired dependence of pooling on
the image spatial layout, however, may severely limit the classification. In this paper, we propose a linear
distance coding (LDC) method to capture the discriminative information lost in traditional coding
methods while simultaneously alleviating the dependence of pooling on the image spatial layout. The
core of the LDC lies in transforming local features of an image into more discriminative distance vectors,
where the robust image-to-class distance is employed. These distance vectors are further encoded into
sparse codes to capture the salient features of the image. The LDC is theoretically and experimentally
shown to be complementary to the traditional coding methods, and thus their combination can achieve
higher classification accuracy. We demonstrate the effectiveness of LDC on six data sets, two of each of
three types (specific object, scene, and general object), i.e., Flower 102 and PFID 61, Scene 15 and
Indoor 67, Caltech 101 and Caltech 256. The results show that our method generally outperforms the
traditional coding methods, and achieves or is comparable to the state-of-the-art performance on these
data sets.
ETPL
DIP-059
What Are We Tracking: A Unified Approach of Tracking and Recognition
Abstract: Tracking is essentially a matching problem. While traditional tracking methods mostly focus on
low-level image correspondences between frames, we argue that high-level semantic correspondences are
indispensable to make tracking more reliable. Based on that, a unified approach of low-level object
tracking and high-level recognition is proposed for single object tracking, in which the target category is

actively recognized during tracking. High-level offline models corresponding to the recognized category
are then adaptively selected and combined with low-level online tracking models so as to achieve better
tracking performance. Extensive experimental results show that our approach outperforms state-of-the-art
online models in many challenging tracking scenarios such as drastic view change, scale change,
background clutter, and morphable objects.
ETPL
DIP-060
Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial
Latent Model
Abstract: In this paper, we combine amplitude and texture statistics of the synthetic aperture radar images
for the purpose of model-based classification. In a finite mixture model, we bring together the Nakagami
densities to model the class amplitudes and a 2-D auto-regressive texture model with t-distributed
regression error to model the textures of the classes. A non-stationary multinomial logistic latent class
label model is used as a mixture density to obtain spatially smooth class segments. The classification
expectation-maximization algorithm is performed to estimate the class parameters and to classify the
pixels. We resort to integrated classification likelihood criterion to determine the number of classes in the
model. We present our results on the classification of the land covers obtained in both supervised and
unsupervised cases processing TerraSAR-X, as well as COSMO-SkyMed data.
ETPL
DIP-061
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image
Segmentation
Abstract: In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation
by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor
depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By
using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In
order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its
objective function. The new algorithm adaptively determines the kernel parameter by using a fast
bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore,
the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental
results on synthetic and real images show that the new algorithm is effective and efficient, and is
relatively independent of this type of noise.
ETPL
DIP-062
Rate-Distortion Optimized Rate Control for Depth Map-Based 3-D Video Coding
Abstract: In this paper, a novel rate control scheme with optimized bits allocation for the 3-D video
coding is proposed. First, we investigate the R-D characteristics of the texture and depth map of the coded
view, as well as the quality dependency between the virtual view and the coded view. Second, an optimal
bit allocation scheme is developed to allocate target bits for both the texture and depth maps of different
views. Meanwhile, a simplified model parameter estimation scheme is adopted to speed up the coding
process. Finally, the experimental results on various 3-D video sequences demonstrate that the proposed
algorithm achieves excellent R-D efficiency and bit rate accuracy compared to benchmark algorithms.
ETPL
DIP-063
Performance Evaluation Methodology for Historical Document Image Binarization
Abstract: Document image binarization is of great importance in the document image analysis and
recognition pipeline since it affects further stages of the recognition process. The evaluation of a
binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by
providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based
binarization evaluation methodology for historical handwritten/machine-printed document images. In the
proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a
weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the
proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms,

background noise, character enlargement, and merging. Several experiments conducted in comparison
with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.
ETPL
DIP-064
Video Quality Pooling Adaptive to Perceptual Distortion Severity
Abstract: It is generally recognized that severe video distortions that are transient in space and/or time
have a large effect on overall perceived video quality. In order to understand this phenomena, we study
the distribution of spatio-temporally local quality scores obtained from several video quality assessment
(VQA) algorithms on videos suffering from compression and lossy transmission over communication
channels. We propose a content adaptive spatial and temporal pooling strategy based on the observed
distribution. Our method adaptively emphasizes “worst” scores along both the spatial and temporal
dimensions of a video sequence and also considers the perceptual effect of large-area cohesive motion
flow such as egomotion. We demonstrate the efficacy of the method by testing it using three different
VQA algorithms on the LIVE Video Quality database and the EPFL-PoliMI video quality database.
ETPL
DIP-065
Modified Gradient Search for Level Set Based Image Segmentation
Abstract: Level set methods are a popular way to solve the image segmentation problem. The solution
contour is found by solving an optimization problem where a cost functional is minimized. Gradient
descent methods are often used to solve this optimization problem since they are very easy to implement
and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often
display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In
this paper, we instead propose using two modified gradient descent methods, one using a momentum term
and one based on resilient propagation. These methods are commonly used in the machine learning
community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the
modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate.
The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the
basic method, and are directly compatible with any type of level set implementation. Downloadable
reference code with examples is available online.
ETPL
DIP-066
Maximum Margin Correlation Filter: A New Approach for Localization and
Classification
Abstract: Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most
of them, the SVM classifier assumes that the object to be classified is centered in the query image, which
might not always be valid, e.g., when locating and classifying a particular class of vehicles in a large
scene. In this paper, we introduce a new classifier called Maximum Margin Correlation Filter (MMCF),
which, while exhibiting the good generalization capabilities of SVM classifiers, is also capable of
localizing objects of interest, thereby avoiding the need for image centering as is usually required in SVM
classifiers. In other words, MMCF can simultaneously localize and classify objects of interest. We test
the efficacy of the proposed classifier on three different tasks: vehicle recognition, eye localization, and
face classification. We demonstrate that MMCF outperforms SVM classifiers as well as well known
correlation filters.
ETPL
DIP-067
Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data
Abstract: This article proposes several improvements to an adaptive fingerprint enhancement method that
is based on contextual filtering. The term adaptive implies that parameters of the method are
automatically adjusted based on the input fingerprint image. Five processing blocks comprise the
adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed

system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1)
preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and
local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and
matched filtering blocks, different forms of order statistical filters are applied. These processing blocks
yield an improved and new adaptive fingerprint image processing method. The performance of the
updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated
toward the NIST developed NBIS software for fingerprint recognition on FVC databases.
ETPL
DIP-068
Objective Quality Assessment of Tone-Mapped Images
Abstract: Tone-mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range
(LDR) images provide practically useful tools for the visualization of HDR images on standard LDR
displays. Different TMOs create different tone-mapped images, and a natural question is which one has
the best quality. Without an appropriate quality measure, different TMOs cannot be compared, and
further improvement is directionless. Subjective rating may be a reliable evaluation method, but it is
expensive and time consuming, and more importantly, is difficult to be embedded into optimization
frameworks. Here we propose an objective quality assessment algorithm for tone-mapped images by
combining: 1) a multiscale signal fidelity measure on the basis of a modified structural similarity index
and 2) a naturalness measure on the basis of intensity statistics of natural images. Validations using
independent subject-rated image databases show good correlations between subjective ranking score and
the proposed tone-mapped image quality index (TMQI). Furthermore, we demonstrate the extended
applications of TMQI using two examples - parameter tuning for TMOs and adaptive fusion of multiple
tone-mapped images.
ETPL
DIP-069
Catching a Rat by Its Edglets
Abstract: Computer vision is a noninvasive method for monitoring laboratory animals. In this article, we
propose a robust tracking method that is capable of extracting a rodent from a frame under uncontrolled
normal laboratory conditions. The method consists of two steps. First, a sliding window combines three
features to coarsely track the animal. Then, it uses the edglets of the rodent to adjust the tracked region to
the animal's boundary. The method achieves an average tracking error that is smaller than a representative
state-of-the-art method.
ETPL
DIP-070
Juxtaposed Color Halftoning Relying on Discrete Lines
Abstract: Most halftoning techniques allow screen dots to overlap. They rely on the assumption that the
inks are transparent, i.e., the inks do not scatter a significant portion of the light back to the air. However,
many special effect inks, such as metallic inks, iridescent inks, or pigmented inks, are not transparent. In
order to create halftone images, halftone dots formed by such inks should be juxtaposed, i.e., printed side
by side. We propose an efficient juxtaposed color halftoning technique for placing any desired number of
colorant layers side by side without overlapping. The method uses a monochrome library of screen
elements made of discrete lines with rational thicknesses. Discrete line juxtaposed color halftoning is
performed efficiently by multiple accesses to the screen element library.
ETPL
DIP-071
Image Noise Level Estimation by Principal Component Analysis
Abstract: The problem of blind noise level estimation arises in many image processing applications, such
as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation
method on the basis of principal component analysis of image blocks. We show that the noise variance
can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13
existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at

least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than
other methods. Our method does not assume the existence of homogeneous areas in the input image and,
hence, can successfully process images containing only textures.
ETPL
DIP-072
Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank
Approach
Abstract: Simultaneous sparse coding (SSC) or nonlocal image representation has shown great potential
in various low-level vision tasks, leading to several state-of-the-art image restoration techniques,
including BM3D and LSSC. However, it still lacks a physically plausible explanation about why SSC is a
better model than conventional sparse coding for the class of natural images. Meanwhile, the problem of
sparsity optimization, especially when tangled with dictionary learning, is computationally difficult to
solve. In this paper, we take a low-rank approach toward SSC and provide a conceptually simple
interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition
of similar packed patches can be viewed as pooling both local and nonlocal information for estimating
signal variances. Such perspective inspires us to develop a new class of image restoration algorithms
called spatially adaptive iterative singular-value thresholding (SAIST). For noise data, SAIST generalizes
the celebrated BayesShrink from local to nonlocal models; for incomplete data, SAIST extends previous
deterministic annealing-based solution to sparsity optimization through incorporating the idea of
dictionary learning. In addition to conceptual simplicity and computational efficiency, SAIST has
achieved highly competent (often better) objective performance compared to several state-of-the-art
methods in image denoising and completion experiments. Our subjective quality results compare
favorably with those obtained by existing techniques, especially at high noise levels and with a large
amount of missing data.
ETPL
DIP-073
Variational Approach for the Fusion of Exposure Bracketed Pairs
Abstract: When taking pictures of a dark scene with artificial lighting, ambient light is not sufficient for
most cameras to obtain both accurate color and detail information. The exposure bracketing feature
usually available in many camera models enables the user to obtain a series of pictures taken in rapid
succession with different exposure times; the implicit idea is that the user picks the best image from this
set. But in many cases, none of these images is good enough; in general, good brightness and color
information are retained from longer-exposure settings, whereas sharp details are obtained from shorter
ones. In this paper, we propose a variational method for automatically combining an exposure-bracketed
pair of images within a single picture that reflects the desired properties of each one. We introduce an
energy functional consisting of two terms, one measuring the difference in edge information with the
short-exposure image and the other measuring the local color difference with a warped version of the
long-exposure image. This method is able to handle camera and subject motion as well as noise, and the
results compare favorably with the state of the art.
ETPL
DIP-074
Image Denoising With Dominant Sets by a Coalitional Game Approach
Abstract: Dominant sets are a new graph partition method for pairwise data clustering proposed by Pavan
and Pelillo. We address the problem of dominant sets with a coalitional game model, in which each data
point is treated as a player and similar data points are encouraged to group together for cooperation. We
propose betrayal and hermit rules to describe the cooperative behaviors among the players. After applying
the betrayal and hermit rules, an optimal and stable graph partition emerges, and all the players in the
partition will not change their groups. For computational feasibility, we design an approximate algorithm
for finding a dominant set of mutually similar players and then apply the algorithm to an application such
as image denoising. In image denoising, every pixel is treated as a player who seeks similar partners
according to its patch appearance in its local neighborhood. By averaging the noisy effects with the

Final Year IEEE Project 2013-2014 - Digital Image Processing Project Title and Abstract

Final Year IEEE Project 2013-2014 - Digital Image Processing Project Title and Abstract

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