Pattern Recognition Vidya Manian Dept. of Electrical and Computer Engineering University of Puerto Rico manian@ece.uprm.

1. Pattern RecognitionVidya ManianDept. of Electrical and Computer EngineeringUniversity of Puerto Ricomanian@ece.uprm.eduINEL 5046, Spring 2007

2. Human Perception • Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe, e.g., • Recognizing a face • Understanding spoken words • Reading handwriting • Distinguishing fresh food from its smell • We would like to give similar capabilities to machines

3. What is a Pattern? • “A pattern is the opposite of a chaos; it is an entity vaguely defined, that could be given a name.” (Watanabe)

4. What is Pattern Recognition? • A pattern is an entity, vaguely defined, that could be given a name, e.g., > Fingerprint image >speech signal > handwritten word >DNA sequence >human face >… • Pattern recognition is the study of how machines can • Observe the environment • Learn to distinguish patterns of interest • Make sound and reasonable decisions about the categories of the patterns

5. Category “A” Category “B” Classification Recognition • Identification of a pattern as a member of a category we already know, or we are familiar with • Classification (known categories) • Clustering (creation of new categories) Clustering

6. Pattern Recognition • Given an input pattern, make a decision about the “category” or “class” of the pattern • Pattern recognition is a very broad subject with many applications • In this course we will study a variety of techniques to solve P.R. problems and discuss their relative strengths and weaknesses

7. Pattern Class • A collection of “similar” (not necessarily identical) objects • A class is defined by class samples (paradigms, exemplars, prototypes) • Inter-class variability • Intra-class variability

8. Pattern Class Model • Different descriptions, which are typically mathematical in form for each class/population • Given a pattern, choose the best-fitting model for it and then assign it to class associated with the model

9. Intra-class and Inter-class Variability The letter “T” in different typefaces Same face under different expression, pose….

10. Inter-class Similarity Characters that look similar Identical twins

11. Pattern Recognition • Having been shown a few positive examples (and perhaps a few negative examples) of a pattern class, the system “learns” to tell whether or not a new object belongs in this class (Watanabe) • COGNITION = Formation of new classes RECOGNITION = known classes

12. Pattern Recognition Applications • Speech recognition • Detection and diagnosis of disease • Remote sensing (terrain classification, tanks detection) • Character recognition • Identification and counting of cells • Fingerprint identification • Web search • Inspection (PC boards, IC masks, textiles)

14. Fish Classification Preprocessing will involve image enhancement, separating touching/occluding fishes and finding the boundary of the fish

15. Length Feature Training (design or learning) Samples

16. Lightness Feature Overlap in the histograms is small compared to length feature

17. Two-dimensional Feature Space (Representation) Cost of misclassification? Two features together are better than individual features

18. Complex Decision Boundary Issue of generalization

19. Boundary With Good Generalization Simplify the decision boundary!

20. Models for Pattern Recognition • Template matching • Statistical (geometric) • Syntactic (structural) • Artificial neural network (biologically motivated?) • Hybrid approach

21. Statistical Pattern Recognition pattern Preprocessing Feature extraction Classification Recognition Training Feature selection Learning Preprocessing Patterns + Class labels

22. x2 x2 x1 x1 Pattern Representation using features • Each pattern is represented as a point in the d-dimensional feature space • Features are domain-specific and be invariant to translation, rotation and scale • Good representation  small intraclass variation, large interclass separation, simple decision rule • No redundant features, too many features and less samples-curse of dimensionality (Huges phenomena)

23. Artificial Neural Networks Massive parallelism is essential for complex pattern recognition tasks (e.g., speech and image recognition) Human take only a few hundred ms for most cognitive tasks; suggests parallel computation Biological networks attempt to achieve good performance via dense interconnection of simple computational elements (neurons) Number of neurons  1010 – 1012 Number of interconnections/neuron  103 – 104 Total number of interconnections  1014

24. Artificial Neural Networks Nodes in neural networks are nonlinear, typically analog where is internal threshold or offset x1 w1 x2 Y (output) xd wd

25. Multilayer Perceptron • Feed-forward nets with one or more layers (hidden) between the input and output nodes • A three-layer net can generate arbitrary complex decision regions • These nets can be trained by back-propagation training algorithm . . . . . . . . . c outputs d inputs First hidden layer NH1 input units Second hidden layer NH2 input units

26. Statistical Pattern Recognition • Patterns represented in a feature space • Statistical model for pattern generation in feature space • Given training patterns from each class, goal is to partition the feature space.

27. Image Analysis and Segmentation (classification) using texture features Classified using Logical operators Aerial photograph of Anasco,PR

28. Classification of color images using texture features Texture mosaic of 3 colored tiles and canvas texture. Classified image

29. Classification in Remote Sensing

30. Sensor

31. Classification of Landsat image of San Juan area, PR using Gabor texture features Classified image using R,G,B Landsat image 7 bands (R, G, B, IR and Thermal)