using ANN and expert system in image recognition systems

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Artificial Intelligence
Seminar Report on
Image Recognition Expert System based on
deep learning
Submitted
by
Name Roll number
Rege PrathameshMilind 1605012
Department of Mechanical Engineering
K. J. Somaiya College of Engineering
Mumbai 400077
Jan/April 2017

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CONTENTS
SR.NO. DESCRIPTION PAGE NO.
1 Introduction 04
2 Literature Review 07
3 Case Study 13
4 Conclusion 17
5 References 18

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Abstract
Image detection systems are gradually being popularized and applied. This paper is to discuss a
new expert system hybridized with deep learning to utilize image detection systems in road safety.
We shall discuss abilities of low power systems to accurately detect high-resolution images.
Secondly we shall discuss knowledge based systems and its’ understanding of image processing.
Thirdly we shall discuss the utilization of Fourier transform in deep learning on an image
recognition system.
Finally we utilize the results of all three studies and apply it to our benefit to detect vehicles
jumping signals at traffic signal crossing

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Introduction
In AI, expert systems are those computer systems which perform decision-making with the same
capacity of human experts. There are used to solve complex problems using mainly if-then rules
as knowledge rather than procedural programming as is the convention.
The expert systems are amongst the first truly successful AI software. The expert systems were
introduced in the 1970s. The first expert system was Stanford Heuristic Programming Project
led by Edward Feigenbaum.
Expert systems were proliferated in the 1980s. The first expert system in design capacity was
Synthesis Integral Design(SID) software program. SID was written in LISP code language.
In the 1990s the idea of the expert system as standalone system vanished. Many of the vendors
(such as SAP, Siebel, Oracle) integrate expert systems with their products so that they go hand in
hand with business automation and integration.
Expert systems are knowledge-based systems. It consists of three subsystems: a user interface, an
inference engine, and a knowledge base. The knowledge base contains the rules and the inference
engine applies them. There are two modes of inferencing: forward and backward chaining.
The various techniques used in inference engine are:
1. Truth maintenance.
2. Hypothetical reasoning.
3. Fuzzy logic.
4. Ontology classification
5. Convolution Neural Networks.
The advantages of Expert systems are:
1. With expert systems, the goal to specify rules is easily intuitive and understood.
2. Ease of maintenance is most obvious benefit.
3. The knowledge base can be updated and extended.

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4. They contain large amount of information.
The disadvantages of Expert systems are:
1. Most common is knowledge acquisition problem (it is tedious).
2. They cannot learn from their mistake and adapt.
3. Mimicking knowledge of expert is difficult.
4. Performance is a problem for expert system using tools such as LISP code.
The most common application of expert system is Image recognition (with help of convolution
neural networks or deep leaning). This is most commonly used in medical field, biology and
mechanical systems.
Image recognition is a classical problem in machine vision in determining if image data contains
specific object or feature.
The image recognition consists of following varieties:
1. Object recognition/classification.
2. Identification.
3. Detection.
The benchmark in image recognition is ImageNet Large Scale Visual Recognition Challenge
(ILSVRC). The ILSVRC has been held annually since 2010. Performance of deep learning in this
challenge is close to that of humans.
The specialized tasks in image recognition are:
1. Content based image retrieval.
2. Pose estimation.
3. Optical character recognition.
4. 2D code reading.
5. Facial recognition.
6. Shape recognition technology.

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Recently image recognition and detection has become common in all fields of technology, such as
social networks and cameras to recognize faces; in medicine and microbiology to detect bacteria,
germs and small obstructions in surgeries; and in phones and mechanical safety systems.
With rise of phones and wireless technologies, use of deep learning for image recognition has been
on the rise. We also see a rise in cameras embedded in many wireless phones, safety systems, and
unmanned aerial vehicles.
It has also been on the rise in automation with increasing usage of robots with compound eyes. It
has also been used in pattern recognition in gaming and other fields.
In this report we will expand on usage of image recognition systems in mechanical safety devices.

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Literature Review
1. Kent Gauen, Rohit Rangan, Anup Mohan, Yung-Hsiang Lu; Wei Liu, Alexander C.
Berg. “Low-Power Image Recognition Challenge”. IEEE Rebooting Computing
Initiative.
Statement: Large-scale use of cameras in battery powered systems has alleviated the
necessity of energy efficiency of cameras in image recognition. LPIRC has decided to set
a benchmark in in comparing solutions of low power image recognition.
In recent years, rise of availability of cameras has led to significant progress in image
recognition. Notwithstanding it also raises the question of efficiency in energy
consumption. Embedded cameras are used in many battery-powered systems for image
recognition where energy efficiency is a critical criterion.
There is no widely accepted benchmark for comparing solutions of low power image
recognition. Currently there is no metric available for comparing in terms of both energy
efficiency and accuracy in recognition.
LPIRC began as a competition to consider both these criteria. It is an offshoot of ILSVRC
and began in 2015.
The benchmark metrics used in LPIRC are:
I. Datasets metric:
At ISLVRC 2013, model from New York University “Overfeat” was proposed. It
used deep learning to simultaneously classify, locate and detect object [1] and
specialized datasets were created. An example is PARASEMPRE in semantic
processing [2].
LPIRC considers object detection. This comes in classification and localization.
The various datasets existing for object detection are: PASCAL, VOC, ImageNet,
ILSVRC and COCO [3][4][5][6].
LPIRC uses ILSVRC dataset as it is the largest one. The dataset for LPIRC is a
subset of ILSVRC.

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II. Evaluation metric:
LPIRC uses m.A.P (mean Average Precision) to measure accuracy of object
detection like ISLVRC [5].
Each detection is in the format (bij, sij) for image Ii and object class Cj; where,
bij is bounding box and sij is the score.
For the bounding box evaluation, it uses IoU.
For x= reported bounding box region.
y= ground truth bounding box region.
IoU =
𝑥∩𝑦
𝑥∪𝑦
(1)
To accommodate smaller objects (less than 25×25 pixels), we lose the threshold
value by giving 5 pixel margin to each side of image.
thr(B) = min (0.5,
𝑤ℎ
(𝑤+10)(ℎ+10)
) (2)
A detection result is true positive if IoU overlaps with ground truth box more than
threshold value defined in equation (2); otherwise it is false positive.
For multiple detection (IoU > 0.5) only the highest score is consideredas true
positive.
The final score is given by
Total score =
𝑚.𝐴.𝑃
𝑇𝑜𝑡𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
(3)
So in conclusion , in the last two years LIPRC has managed to establish itself as a
benchmark for low power image detection. There has been has improvements in
both m.A.P and energy efficiency in the last two years.

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2. Takashi Matsuyama, “Knowledge-Based Aerial Image Understanding Systems and
Expert Systems for Image Processing”. IEEE Transactions on Geoscience and Remote
sensing, Vol. GE-25, NO. 3, MAY 1987
Statement: AI, in the form of knowledge based systems, has an extensive role in automatic
interpretation of remotely sensed imagery. The development of space aeronautics and
drone technologies have led to extensive development of expert systems in aerial image
understanding.
Automatic interpretation of aerial photo-graphs is now widely preferred and used. The
various analysis methods used are:
i. Statistical classification methods of pixel understanding.
ii. Target direction by template matching.
iii. Shape & texture analysis by image processing.
iv. Use of structural and contextual information.
v. Image understanding of aerial photographs.
Knowledge base and reasoning strategy are major topics of research in AI and many
techniques have been developed: semantic networks and frames, logical inference and so
on. They are used to solve problems requiring expertise [7].
They are flexible and are used to solve following problems:
i. Noise in input image data & errors in image recognition.
ii. Ill-defined problems.
iii. Limited information available.
iv. Requirement of versatile capabilities of geometric reasoning.
A blackboard model for aerial image allows flexible integration of diverse object detection
models. It is the database where all information is stored. Since all image recognition
results are written in blackboard, all subsystems can see them to detect new objects.

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However sophisticated control structures are required to realize flexible image
understanding [8].
It incorporates:
i. Focus of attention to confine spatial domains.
ii. Conflict resolution.
iii. Error correction.
ACRONYM [9] is used to detect complex 3D objects which are represented by frames. It
then matches models and image features. Since it is difficult to detect features using
bottom-up analysis alone, it also integrates top-down analysis.
SIGMA is used to represent about hypothesis [10].
In SIGMA three levels of reasoning are identified:
i. Reasoning about structure and spatial relations between objects.
ii. Reasoning about transformation of objects.
iii. Reasoning about image segmentation.
Geographic information systems & aerial image understanding complement each other.
In conclusion, to realize flexibility in integration, we solve problems using data mapping,
data structuring, accurate correspondence and map guided photo transformation.

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3. Yuki Kamikubo, Minoru Watanabe, Shoji Kawahito, “Image recognition system
using an optical Fourier transform on a dynamically reconfigurable vision
architecture”
Statement: Recently, several varieties of image recognition using Fourier transform have
been proposed. The benefit of using Fourier transform is its position independent image
recognition capability. Notwithstanding the operation of Fourier transform of high
resolution is heavy. Hence it is needed to shorten the time period using dynamic
reconfiguration.
Demand of high speed image recognition for development of autonomous vehicles, aircraft
and robots has been increasing [11]. The frame rates used for image recognition are limited
to 30 fps; but the frame rates required are at the rates higher than 1000 fps.
Image recognition are always executed sequentially.
Numerous template images are stored in memory in advance. Template matching is
executed between external images and template images. Recognition slows if various
images are recognized simultaneously.
To remove this bottleneck, an optoelectronic device with holographic memory is
introduced.
Multiple template images are stored in this large holographic memory. since the device has
massive parallel optical connection (> 1 million), template information can be read out
quickly in a very short period. Yet in a position independent image recognition operation,
which is mandatorily required in a real-world operation, template matching takes a long
time. This can be resolved by Fourier transform [12] [13] [14].
Fourier transform is well known to be useful for position independent image recognition.
It is introduced outside VLSI technologies. It is used in dynamically reconfigurable vision
chip. The image is focused on PAM-SLM which is an optical read-in and read-out device.
The coherent image passes through a set of lens. After this, the power spectrum of image
is received on photodiode array. Fourier transform is executed constantly and automatically
and phot spectrum can be position independent.

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Use of photodiode arrays reduces time required drastically; only 1 ms elapses for transfer
of 100000 templates and its matching.
A Fourier transform is calculated theoretically as follows:
The amplitude φ(x,y) of diffraction is calculated as
φ(x,y) α ∬ 𝐼(x0,y0)L(x0,y0)exp[jkr]dx0dy0
∞
−∞
In Fresnel region, r can be approximated as
r ~ f +
( 𝑥0−𝑥)2
+(𝑦0−𝑦)^2
2𝑓
(4)
where
f is distance between lens plane and observation plane
k is wave number
(x0, y0) is co-ordinates of lens plane
(x, y) is co-ordinate of observation plane
I(x0, y0) is an image information
L(x0, y0) is phase modulation of lens
L(x0, y0) = exp[-j
𝑘
2𝑓
(x02+y02)]
Fourier transform is achieved as
φ(x, y) α ∬ 𝐼( 𝑥0, 𝑦0)exp[−j
𝑘
𝑓
(x0x+ y0y)]dx0dy0
∞
−∞
The diffraction light intensity is calculated as
P(x, y) = φ(x, y)φ*(x, y) (5)
(*) denotes complex conjugate
The result P(x, y) is power spectrum of an image.
In conclusion Fourier transform dynamically reconfigurable vision architecture recognizes
three artificial images by detecting power spectrum images. The architecture use PAL-
SLM and a lens. Fourier transform can be executed in real time. It is useful to extract
features of power spectrum information of each image in real time.

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Case Study
Problem Statement: To address the need of road safety at traffic signals by installing image
detection expert systems.
This problem is a common one suffered universally. About 1214 road crashes occur in India daily,
with two-wheelers accounting for 25% of total road crash deaths [15] [16]. A major cause for this
is jumping of red lights [15]. People breaking the signal and not getting caught tend to have the
urge to break it again. As such there is need to have high speed detection systems.
Figure 1. Road traffic deaths in India 1970-2014 (Source: NCRB) [15].
Figure 2. Cars and MTW registered in India by year (Source: Transport Research
Wing 2014) [15] [16].

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This is where image recognition systems come in. They are low power standalone systems. The
knowledge base can be linked with DNN. The output of traffic signal and embedded cameras are
connected to input of DNN and the output to inference engine [17].
The vehicle terminal fusion information is given below:
Figure. 3. Vehicle terminal fusion information [17]
The knowledge base can be linked wirelessly on a cloud-interface with a scanning module
connected to Aadhaar/RTO database. The module will scan the database to find the match for
license plate no. shown in image; thereby identifying the owner of the vehicle. The culprit can then
be caught and handed over to the law. This will help reduce the accident rate.
Creation of Rule base:
The rule base is created using forward chaining method [18] [19] [20].
R1: IF RED THEN STOP.
R2: IF GREEN THEN GO.
R3: IF YELLOW AND STOP OR GO THEN GO.
R4: IF RED AND STOP THEN NO PHOTO.

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R5: IF RED AND GO THEN PHOTO.
R6: IF GREEN AND STOP THEN PHOTO.
R7: IF GREEN AND GO THEN NO PHOTO.
R8: IF YELLOW THEN NO PHOTO.
The block diagram will become:
Figure. 4. Forward chained rule base.
For a random data set of 1500 images collected the precision rate is given below recorded every 5
minutes:

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Figure. 5. Absolute prediction error (Before peak hour)
Figure. 6. Absolute prediction error (Post peak hour)

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Conclusion
Based on the results this report conclude that an image detection system can decrease precision
error by a margin of 20%. This will help prevent future accidents and instill a sense of road safety
in people. DNN will help compensate the weakness of expert systems by gradually adapting to
every condition.

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