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Pattern Recognition

Pattern Recognition. Dr. R. J. Ramteke Associate Professor, Dept. of Computer Science North Maharashtra University, Jalgaon. More refined and abstract. Wisdom. Knowledge. Information. Data. Information Hierarchy. Information Hierarchy. Data The raw material of information

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Pattern Recognition

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  1. Pattern Recognition • Dr. R. J. Ramteke • Associate Professor, • Dept. of Computer Science • North Maharashtra University, Jalgaon

  2. More refined and abstract Wisdom Knowledge Information Data Information Hierarchy

  3. Information Hierarchy • Data • The raw material of information • EX - 98.6º F, 99.5º F, 100.3º F, 101º F, … • Information • Data organized and presented in a particular manner • EX - Body temperature: 98.6º F, 99.5º F, 100.3º F… • Knowledge • Information that can be acted upon • EX - If you have a temperature above 100º F, you most likely have a fever • Wisdom • Distilled and integrated knowledge • Demonstrative of high-level “understanding” • EX - If you don’t feel well, go see a doctor

  4. Pattern Recognition • Pattern : A Visible Entity • Recognition = Re+ Cognition • Learning • < Re-Enforcement of • Learning > • Labelling

  5. Pattern Recognition:An Overview • Pattern recognition is characteristics to all living organisms, however, creatures recognize differently • We have many ways to recognize the given patterns • Human by sight, voice (sound recognition), walking style (tracking), his vehicle (context based ) etc, • Dog recognizes a human or animal by smelling • Blind person recognizes the objects by touching

  6. Pattern Recognition:An Overview • Pattern – the object which is inspected for the recognition process is called a pattern • Usually we refer to pattern as a description of an object which we want to recognize • Pattern recognition problem is a problem of discriminating between different populations • Eg. Tall and Thin, Tall and Fat, Short and Thin, and Short and Fat • Recognition process thus, turns into classification (if we consider the age as feature and height and weight as a features)

  7. Pattern Recognition:An Overview • Pattern recognition system should be able to obtain an unknown incoming pattern and classify it in one (or more) of several given classes . • The goal of PR is classification of patterns Eg. Decision function • d(x) > 0 x belong to C1 and d(x) < 0 x belong to C2 • where d(x) = 0 is hyper plane is called decision boundary and C1 and C2 are two classes.

  8. Mechanization of Cognition i.e., Both ‘Learning’ + ‘Labelling’ subsequently. Learning? Learning to Label. Labelling? Identifying a pattern as a member of a class to which it belongs.

  9. Pattern Recognition Techniques to classify or describe What : Samples/Objects/Patterns How : By means of the measured properties called features. Thus, PR Data Acquisition + Data Analysis

  10. The major approaches to PR are • The Statistical PR approach • Syntactic PR approach and • Neural network has provided as third approach • Types of Patterns: • Spatial patterns (patterns are located in space) • Characters in character recognition . • Temporal patterns( Distributed in time ) • Speech Recognition • Abstract patterns (patterns are distributed neither in space nor time) • Classification of people based on psychological tests.

  11. Applications of Pattern Recognition • Object Recognition • Document Image Processing • Content Based Image Retrieval • Image Mosaicing • Character /Numeral Recognition • Face Recognition • Finger Print Identification • Medical Diagnosis • Signature Verification • Industrial Inspection • Video Indexing • Robot Manipulation • Computer Vision

  12. If the Patterns are Pictures/ images, then the PR stages are : • Image Acquisition • Image Enhancement • Image Segmentation • Image Feature Extraction • Image Matching

  13. Stages in Pattern Recognition • Delineation Feature Extraction Descriptive features Discriminatingfeatures • Representation • Classification

  14. Feature Extraction : Feature : An extractable measurement. Why ? : For Description. What Feature ? : Depends on purpose of classification. How many ? : Depends on Qualities of the PR System. When ? : 1. Cognition 2. Recognition How ? : ??!!!

  15. Examples (1) : Feature Extraction • Objects : • straight line • not a straight line • circle

  16. Examples (2) : Feature Extraction Objects : A B C D E F

  17. Feature? Line and Curve Segments

  18. Knowledge Acquired

  19. Machine Learning through Vision? Re Learning A COW A COW WITH THREE LEGS AND TWO TAILS

  20. Thank you one and all Dr. R. J. Ramteke rakeshramteke@yahoo.co.in 9890688672

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