Dr' Claude C' Chibelushi

1
Image Processing, Computer Vision, and Pattern
Recognition
Fac. of Comp., Eng. Tech. Staffordshire
University
Statistical Pattern RecognitionPart II
Classical Model
Dr. Claude C. Chibelushi
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Outline

• Introduction
• Classical Pattern Recognition Model
• Feature Extraction
• Classification
• Applications
• Optical Character Recognition
• Others
• Summary

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Introduction

• Pattern recognition often consists of sequence of
  processes
• often configured according to classical pattern
  recognition model

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Classical Recognition Model
Simplified block diagram
Facial image
Example
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Classical Recognition Model

• Performance issues
• All stages of recognition pipeline, and their
  connection, affect performance
• typical performance measures recognition
  accuracy, speed, storage requirements
• optimisation of components/connections often
  required
• careful selection / design / implementation of
• data capture equipment / environment
• processing techniques

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Feature Extraction

• Aim to capture discriminant characteristics of
  pattern
• Extracts pattern descriptors from raw data
• descriptors should contain information most
  relevant to recognition task
• descriptors may be numerical (quantitative ) or
  linguistic
• group of numerical descriptors often known as
  feature vector

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Feature Extraction

• Common features for computer vision
• Shape descriptors
• external (e.g. boundary) internal (e.g. holes)
• Surface descriptors
• texture, brightness, colour, ...
• Spatial configuration descriptors
• arrangement of basic elements
• Temporal configuration descriptors
• deformation or motion of basic elements

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Feature Extraction

• Example
• Pattern recognition application gender detection
• Classes male, female

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Feature Extraction

• Example
• Chosen features height, silhouette area

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Feature Extraction
Graphical representation of feature
distribution Example data set of 5 male and 5
female subjects
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Feature Extraction

• Graphical representation of feature distribution
• Example (ctd.) Feature plot 2D feature space

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Classification

• Aim to identify class (category ) to which
  unknown pattern belongs
• Wide variety of classifiers
• Classifier selection is problem-dependent
• use simple classifier if effective

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Classification

• Some classifiers
• Minimum-distance classifier
• classification based on distance from
  class-prototype (e.g. average) pattern
• closest prototype determines class
• k-nearest neighbour classifier
• classification based on distance from class
  patterns (or clusters)
• closest k patterns (or clusters) determine class

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Classification

• Some classifiers
• Bayesian classifier
• classification based on probability of belonging
  to class
• most likely class
• Artificial neural network classifier
• classification based on neuron activations (shown
  to relate to class probability)
• most likely class

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Classification
Minimum-distance classifier
2D feature space
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Classification

• k-nearest neighbour classifier

2D feature space
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Classification

• Some distance metrics
• (for distance-based classifiers)
• Measure similarity between unknown pattern and
  prototype pattern
• based on differences between corresponding
  features in both patterns, e.g.
• Euclidean distance sum of squares of differences
• City-block (Manhattan or taxi-cab) distance sum
  of absolute values of differences

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Classification

• Decision boundary for
• minimum-distance classifier

2D feature space
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Classification

• Limitations of minimum-distance classifier
• Prone to misclassification for
• high feature correlation
• problems requiring non-linear decision boundary,
  e.g.
• curved decision boundary
• data with subclasses (i.e. clusters)
• intricate decision boundary

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Classification
2D feature space
Feature correlation
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Classification
2D feature space
Curved decision boundary
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Classification
2D feature space
Distinct subclasses
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Classification
2D feature space
Complex decision boundary
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Classification

• Classifier training
• Data-driven extraction of salient class
  characteristics
• supervised training
• class labels used during training
• unsupervised training
• class labels not used during training (e.g.
  clustering)

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Classification

• Classifier testing
• Testing estimation of recognition accuracy
• often uses real data simulation may be used
  (Monte Carlo)
• Accuracy measure
• error rate (often expressed as percentage)
• e.g. correct recognition rate, insertion rate,
  false acceptance rate, false rejection rate, ...

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Optical Character Recognition
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Optical Character Recognition
Generic OCR system
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Optical Character Recognition

• Feature extraction methods
• Spatial domain to frequency domain transform
• Hartley, Fourier, or other transform
• Statistics
• mean, variance projection histograms
  orientation histograms

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Optical Character Recognition

• Feature extraction methods
• Miscellaneous
• geometric measures
• ratio of width and height of bounding box, ...
• description of skeletonised characters
• graph description comprising line segments (e.g.
  strokes of Chinese characters)
• number of L,T, or X junctions, ...

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Optical Character Recognition

• Feature extraction methods

Projection histograms
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Optical Character Recognition

• OCR examples
• (see AALs book)

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Other Recognition Applications

• Sample
• recognition of faces or facial expressions
• recognition of body movement (gestures, gait)
• recognition of handwriting (text, signature)
• industrial inspection
• autonomous vehicles, traffic monitoring
• ...
• (Exercise identify architectural components for
  these applications, and discuss factors affecting
  performance)

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Summary

• Classical pattern recognition model
• pre/post-processing
• feature extraction
• classification
• Feature extraction representation of
  discriminant pattern characteristics
• Classification
• wide variety of classifiers
• supervised or unsupervised classifier training

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Summary

• Components of generic OCR system
• image capture, image pre-processing, feature
  extraction, classification, post- processing
• Wide variety of features for OCR, e.g.
• frequency-domain representation
• statistical or geometric measurements
• skeleton descriptors
• ...