2. Contents
01_ Philosophical Debates on AI
02_ Pattern Recognition (PR)
03_ Features and Patterns
04_ Components of PR and Design Cycle
05_ Category of PR and Classifiers
06_ Performance Evaluation of PR Algorithms
07_ Approaches of PR and its Application Areas
08_ Example of PR Applications
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3. 01_Philosophical Debates on AI
Questions
Is computer merely a calculating machine?
Can computer think and understand languages like human?
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4. 01_Philosophical Debates on AI
Positive opinions on the possibility of AI
A.M. Turing(1912~1954) Imitation Game
Negative opinions on the possibility of AI
John Searle(1932~ ) Chinese Room Arguments
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5. 02_Definition of Pattern Recognition
What is PR?
An area of AI that deals with the problems to make computable machines
(Turing Machines) to recognize certain objects
Cognitive
Science
AI
PR
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6. 03_Features and Patterns
What is feature?
Discernible aspects, qualities, characteristics that a certain object has
What is pattern?
A set of traits or features of individual objects
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8. 04_Components of PR and Design Cycle
Components of PR System and its Process
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9. 04_ Components of PR and Design Cycle
Design steps of PR system
Step 1 : Data gathering
Most time-consuming tedious process in PR tasks
Necessary step to ensure stable PR performance
For stable performance, we need to consider how many samples are needed before
the gathering.
Step 2 : Feature selection
Essential part regarding PR system’s performance
We need to decide what features to choose through sufficient prior analysis on the
object patters.
Step 3 : Model selection
To decide what approach (model and algorithm) is to be constructed and applied
Need prior knowledge on the features
Need to set up parameters for the model according to the approach
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10. 04_ Components of PR and Design Cycle
Step 4 : Learning
Using the feature sets extracted from the collected data and chosen models, the
learning algorithm generates or fills up the model (or hypothesis, classifier)
According to the methods, there are supervised learning, unsupervised learning and
reinforcement learning.
Step 5 : Recognition
Given a new feature set, the generated hypothesis decide a class or category that
the feature set belongs to.
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11. 05_ Category of PR and Classifiers
Categories of problems
Classification
In classification problem, the system needs to output one label in a set of finite
number of labels.
Regression
Generalized version of classification
Through regression, the PR system will return a real value score (usually between 0
and 1)
Clustering
The problem of organizing a small number of multiple groups from a certain set
The output of clustering system is a set of pairs (example and its class).
The clustering can be processed in an hierarchical manner such as in phylogenetic
tree.
Description
The problem of expressing an object using a set of a prototype or primitive terms
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12. 05_ Category of PR and Classifiers
Classifier
Most classification task in PR is done by classifiers
Classification is to partitioning a feature space composed of feature vectors into
decision regions of nominal classes.
We call the boundaries of the regions as decision boundaries
Classification of a feature vector x is to decide what decision region the feature
vector belongs to, and to assign x to the class that represents the region
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13. 05_ Category of PR and Classifiers
Classifier can be represented as a set of discriminant functions
∀j=i , if 𝑔 𝑖 𝑥 > 𝑔 𝑗 𝑥 , then we decide that the feature vector 𝑥 ∈ class 𝜔 𝑖
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16. 06_ Performance Evaluation of PR Algorithms
AUROC
Ares under the region of ROC Curve
Closer the curve to top-left corner, more accurate the recognition algorithm
The performance can be evaluated by the amount of AUROC
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17. 07_ Approaches of PR and its Application Areas
Approaches of PR
Template matching
Oldest and easiest
First, prepare the template for the object to compare.
Normalize the pattern to recognize for matching it with the template.
And calculate similarity value such as cross-correlation or distance to perform the
recognition
Most important task is to prepare the most general template that explains all the samples
in a certain category.
Fast running time, but weak in variation of features
Statistical approaches
Decide the class of unknown pattern bases on decision boundaries of pattern sets.
Each of the pattern sets represent a certain class.
The statistical model of the patterns is a probability density function 𝑃 𝑥|𝑐 𝑖 .
Learning is a process of creating a probability density function and calculating its
parameters for each class
Neural networks
Model the relation of connection and integration of the biological neurons
Calculate the response process of neural network for input stimulus
Classify patterns based on the responses
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18. 07_ Approaches of PR and its Application Areas
Knowledge of the patterns is stored as weights that represent the connection
strength of synapse.
Learning is performed similar to biological ways, but the learning process is not a
serial algorithm.
The learned knowledge is considered as a black box.
Minimal need for prior knowledge.
With sufficient number of neurons, theoretically any complicated decision
boundaries can be constructed, so this approach is very attractive.
Structural approaches
Instead of quantitative features, we consider the relationship among the basic
prototypes what construct the pattern.
Examples: Character, Fingerprint, Chromosome
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19. 07_ Approaches of PR and its Application Areas
Approaches of PR
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20. 07_ Approaches of PR and its Application Areas
Applications of PR
Character recognition
Convert a scanned text image into character codes which can be edited in a
computer
Mail classification, Handwriting recognition, Check and banknote recognition,
License plate recognition
Biological recognition and human behavioral pattern recognition
Voice recognition, fingerprint recognition, face recognition, DNA mapping, walking
pattern analysis and classification, utterance habit analysis and classification
Diagnostic systems
Car malfunction, medical diagnostics, EEG, ECG signal analysis and classification, X-
Ray image pattern recognition
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21. 07_ Approaches of PR and its Application Areas
Prediction system
Weather forecasting based on satellite data, earthquake pattern analysis and
earthquake prediction, stock price prediction, etc.
Security and military area
Intrusion detection based on network traffic pattern analysis, security screening
system, search and attack of terrorist camp and targets using satellite terrain
image analysis, radar signal classification, Identification Friend or Foe (IFF)
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22. 07_ Approaches of PR and its Application Areas
Related Areas Application Areas
•Adaptive signal processing •Image
•Machine learning processing/segmentation
•Artificial Neural networks •Computer Vision
•Robotics and Vision •Speech recognition
•Cognitive science •Automatic target recognition
•Mathematical Statistics •Optical character recognition
•Nonlinear optimization •Seismic Analysis
•Exploratory Data analysis •Man-machine interaction
•Fuzzy and Genetic System •Bio recognition (fingerprint,
•Detection and Estimation vein, iris)
Theory •Industrial inspection
•Formal language •Financial forecast
•Structural modeling •Medical analysis
•Biological cybernetics •ECG signal analysis
•Computational neuroscience
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23. 08_ Example of PR Applications
Simple English character recognition system
feature V : # of vertical lines
feature H : # of horizontal lines
feature O : # of slopes
feature C : # of curves
Feature
Character
V H O C
L 1 1 0 0
P 1 0 0 1
O 0 0 0 1
E 1 3 0 0
Q 0 0 1 1
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24. 08_ Example of PR Applications
Automatic fish classification (Sea Bass or Salmon)
A: Conveyor belt for fish
B: Conveyor belt for classified fish
C : Robot arm for grabbing fish
D: Machine vision system with CCD camera
E : Computer that analyze fish image and control the robot arm
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25. 08_ Example of PR Applications
Automatic fish classification
Assume that fish is either salmon or sea bass
Using machine vision system for acquiring new fish image
Normalize the intensities of new fish image using image processing algorithm
Segment fish from the background in the image processing process
Using the prior knowledge that sea bass is bigger than salmon, extract features in
the image to measure the length of the new fish
From the training samples of the two fish categories, calculate the distribution of
the length, and decide the threshold of decision boundary that minimize the
classification error
Accuracy : 60%
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26. 08_ Example of PR Applications
Adding features for enhancing recognition rate
The accuracy should be over 95% for stable pattern recognition system
We find that average intensity level is a good feature.
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27. 08_ Example of PR Applications
Enhancing the recognition rate
We generate 2 dimensional feature vector with length and average intensity.
Using a simple linear discriminant function, we enhance the recognition rate.
Accuracy : 95.7%
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28. 08_ Example of PR Applications
Cost vs. Classification Rate
To minimize the cost, we adjust the decision boundary
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29. 08_Example of PR Applications
Generalization problem
Using neural network, the performance can be enhanced to 99.9975%
Is this a good result?
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