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Pattern Recognition

Pattern Recognition. NTUEE 高奕豪 2005/4/14. Outline. Introduction Definition, Examples, Related Fields, System, and Design Approaches Bayesian, Hidden Markov Model, Neural Network, Decision Tree Modern Applications Face, Handwriting, Fingerprint, Speech.

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Pattern Recognition

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  1. Pattern Recognition NTUEE 高奕豪 2005/4/14

  2. Outline • Introduction • Definition, Examples, Related Fields, System, and Design • Approaches • Bayesian, Hidden Markov Model, Neural Network, Decision Tree • Modern Applications • Face, Handwriting, Fingerprint, Speech

  3. Introduction:What is Pattern Recognition? • “The assignment of a physical object or event to one of several pre-specified categories” –Duda and Hart, author of Pattern Classification • “Given some examples of complex signals and the correct decisions for them, make decisions automatically for a stream of future examples” –Ripley, Oxford University • “The process of giving names ω to observations x”, –Schürmann • “Pattern Recognition is concerned with answering the question ‘What is this?’ “ –Morse

  4. Introduction:Typical Examples • Machine vision • Character recognition • Computer aided diagnosis • Speech recognition

  5. Introduction: Related Field • Adaptive Signal Processing • Machine Learning • Artificial Neural Networks • Mathematical Statistics • Fuzzy and Genetic systems • Formal Languages • Biological Cybernetics • Computational Neuroscience • And so on…

  6. Introduction:A particular example

  7. Pattern Recognition System • Sensing • Segmentation • Feature Extraction • Classification • Post Processing

  8. Pattern Feature • Any Distinctive aspect, quality, or characteristics.

  9. Pattern Recognition System Design Cycle Collect Data Choose Features Choose Model Train Classifier Evaluate Classifier

  10. Approach

  11. Approach • Bayesian Decision • Hidden Markov Model • Multilayer Neural Network • Decision Tree

  12. Bayesian Decision • Provide all relevant probability and cost • Bayes Formula: P(ωj|x) = P(x|ωj) P(ωj) / P(x) (posteriori = likelihood×prior÷evidence) • Bayes Decision Rule: Decide ω1 if P(ω1|x) > P(ω2|x) , otherwise decide ω2

  13. Bayesian Decision • Example: Given P(ω1)=2/3, P(ω2)=1/3 P(x|ω) P(ω|x)

  14. Hidden Markov Model • Useful for problems that have an inherent temporality • Markov Model: A set of states with transition probability

  15. Hidden Markov Model • A state ω(t) may emit some visible symbol v(t) • aij=P(ωj(t+1)|ωi(t) • bij = P(vk(t)|ωj(t))

  16. Hidden Markov Model • Evaluation Problem • Given a HMM, determine the probability that a particular sequence of visible states VT was generated by it • Decoding Problem • Given VT, determine the most likely sequence of hidden states ωT that led it • Learning Problem • Given the number of states and a set of visible symbols, determine aij and bij

  17. Hidden Markov Model • Evaluation: • Brute force Enumeration O(T C^T) • Solution: Dynamic Programming

  18. Hidden Markov Model • Viterbi Algorithm State 0 1 2 T-2 T-1 Time

  19. Hidden Markov Model • Search for “Yes”/”No”

  20. Multilayer Neural Network • Implement linear discriminants in a space where the inputs have been mapped nonlinearly • The nonlinearity can be learned from training data

  21. Multilayer Neural Network

  22. Multilayer Neural Network

  23. Decision Tree • A classification problem involves nominal data • Property D-Tuple: • Fruit: color, texture, shiny, taste • Apple = { red, shiny, sweet, medium} • String, DNA

  24. Decision Tree

  25. Decision Tree

  26. Decision Tree

  27. Modern Applications • Face Recognition • Fingerprint Recognition • Handwriting Recognition • Speech Recognition

  28. Face Recognition • Recognition and Coding, MIT Media Lab

  29. Face Recognition

  30. Face Recognition • FaceCheck, C-VIS

  31. Face Recognition

  32. Fingerprint Recognition • Optical/Charge • 10~40 feature points, transformed into feature vector • Typically, • 500 dpi • FAR<25/1,000,000 • FRR<3/100 • 150 USD

  33. Fingerprint Recognition

  34. Handwriting Recognition • Optical Character Recognition: • Printed, certain fonts • Intelligent Character Recognition • Constrained text entry • Natural Handwriting Recognition • Breakdown, check by linguistic rules

  35. Handwriting Recognition • EverNote, CA, USA

  36. Handwriting Recognition

  37. Speech Recognition

  38. Speech Recognition

  39. Reference • Pattern Classification, 2/e, Richard O. Duda, Peter E. Hart, David G. Stork • http://faculty.cs.tamu.edu/rgutier/ • http://vismod.media.mit.edu/vismod/demos/facerec/system.html • http://www.frvt.org/FRVT2002/Default.htm • http://www.c-vis.com/htd/fsnapr.html • http://sa.ylib.com/circus/circusshow.asp?FDocNo=200&CL=9 • http://www.wave-report.com/other-html-files/parascript-nhr.htm • http://bias.csr.unibo.it/fvc2004/ • http://www.evernote.com/en/ • http://www.microsoft.com/resources/casestudies/CaseStudy.asp?CaseStudyID=16039 • http://www-306.ibm.com/software/voice/viavoice/index.shtml

  40. Thank you for your attention.

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