2. 240-572: Chapter 1: Introduction 1 Montri Karnjanadecha montri@coe.psu.ac.th http://fivedots.coe.psu. ac.th/~montri 240-650 Principles of Pattern Recognition

3. 240-572: Chapter 1: Introduction 2 Chapter 1 Introduction

4. 240-572: Chapter 1: Introduction 3 Outline Pattern Recognition System The Design Cycle Learning and Adaptation Read Chapter 1 (Duda, Hart, and Stork)

5. 240-572: Chapter 1: Introduction 4 Motivations Pattern recognition has many very valuable civil as well as military applications –Automated target recognition –Automated Processing Systems –New Human Computer Interface –Biometrics

6. 240-572: Chapter 1: Introduction 5 Handwritten Address Interpretation System HWAI - http://www.cedar.buffalo.edu/HWAI/ –The HWAI (Handwritten Address Interpretation) System was developed at Center of Excellence for Document Analysis and Recognition (CEDAR) at University at Buffalo, The State University of New York. It resulted from many years of research at CEDAR on the problems of Address Block location, Handwritten Digit/Character/Word Recognition, Database Compression, Information Retrieval, Real-Time Image Processing, and Loosely- Coupled Multiprocessing. –The following presentation is based on the demonstration pages at HWAI

7. 240-572: Chapter 1: Introduction 6 Handwritten Address Interpretation System – cont. Step 1: Digitization

8. 240-572: Chapter 1: Introduction 7 Handwritten Address Interpretation System – Cont. Step 2: Address Block Location

9. 240-572: Chapter 1: Introduction 8 Handwritten Address Interpretation System – Cont. Step 3: Address Extraction

10. 240-572: Chapter 1: Introduction 9 Handwritten Address Interpretation System – Cont. Step 4: Binarization

11. 240-572: Chapter 1: Introduction 10 Handwritten Address Interpretation System – Cont. Step 5: Line Separation

12. 240-572: Chapter 1: Introduction 11 Handwritten Address Interpretation System – Cont. Step 6: Address Parsing

13. 240-572: Chapter 1: Introduction 12 Handwritten Address Interpretation System – Cont. Step 7: Recognition –(a) State Abbreviation Recognition

14. 240-572: Chapter 1: Introduction 13 Handwritten Address Interpretation System – Cont. Step 7: Recognition –(b) ZIP Code Recognition

15. 240-572: Chapter 1: Introduction 14 Handwritten Address Interpretation System – Cont. Step 7: Recognition –(c) Street Number Recognition

16. 240-572: Chapter 1: Introduction 15 Handwritten Address Interpretation System – Cont. Step 8: Street Name Recognition

17. 240-572: Chapter 1: Introduction 16 Handwritten Address Interpretation System – Cont. Step 9: Delivery Point Codes

18. 240-572: Chapter 1: Introduction 17 Handwritten Address Interpretation System – Cont. Step 10: Bar coding

19. 240-572: Chapter 1: Introduction 18 IBM Voice Systems Voice enabling e-bussiness http://www-4.ibm.com/software/speech/enterprise/dcenter/demo_0.html –Get information through speech recognition software ViaVoice

20. 240-572: Chapter 1: Introduction 19 Machine Demonstrates Superhuman Speech Recognition Abilities Developed by Jim-Shih Liaw and Theodore W. Berger at University of Southern California The following is the claim –“University of Southern California biomedical engineers have created the world's first machine system that can recognize spoken words better than humans can. A fundamental rethinking of a long-underperforming computer architecture led to their achievement”.

21. 240-572: Chapter 1: Introduction 20

22. 240-572: Chapter 1: Introduction 21 Statistical Pattern Recognition In statistical pattern recognition, recognition is done by classifying the input (represented as a set of measurements) into predefined categories The core questions we want to address –What is the best we can do (statistically) when a set of measurements is given for input? –Which measurements should be used if we can choose a subset of all the measurements?

23. 240-572: Chapter 1: Introduction 22 A Simple Example Suppose that we are given two classes  1 and  2 –P(  1 ) = 0.7 –P(  2 ) = 0.3 –No measurement is given Guessing –What shall we do to recognize a given input? –What is the best we can do statistically? Why?

24. 240-572: Chapter 1: Introduction 23 An Introductory Example

25. 240-572: Chapter 1: Introduction 24 Terminology Features –Measurements available to the pattern recognition system Models –Each class is represented by a description in mathematical forms, called a model Preprocessing –Segmentation Isolate the object of interest from the background and other objects

26. 240-572: Chapter 1: Introduction 25 Terminology - cont. Feature extraction –Is the measuring process that produces the measurements, or called features Training samples –Models for classes are often specified by samples with known labels. These samples are called training samples

27. 240-572: Chapter 1: Introduction 26 Terminology - cont. Cost/risk –The cost of a decision associated with the recognition result Decision theory –The theory on optimal decision rules

28. 240-572: Chapter 1: Introduction 27 Terminology - cont. Decision boundary –Boundaries in the feature space of regions with different classes (decisions)

29. 240-572: Chapter 1: Introduction 28 Terminology - cont. Generalization –While classes can be specified by training samples with known labels, the goal of a recognition system is to recognize novel inputs –When a recognition system is over-fitted to training samples, it may give bad performance for typical inputs

30. 240-572: Chapter 1: Introduction 29 Terminology - cont. Generalization - continued

31. 240-572: Chapter 1: Introduction 30 Terminology - cont. Generalization - continued

32. 240-572: Chapter 1: Introduction 31 Terminology - cont. Generalization - continued

33. 240-572: Chapter 1: Introduction 32 Terminology - cont. - Analysis by synthesis model

34. 240-572: Chapter 1: Introduction 33 Designing a Pattern Recognition System

35. 240-572: Chapter 1: Introduction 34 Designing a Pattern Recognition System

36. 240-572: Chapter 1: Introduction 35 Steps in a Pattern Recognition System Sensing –Measuring of features, such as a digital camera, or a microphone –We assume the measurements are given Segmentation and grouping –In the fish example, we have to isolate a fish from other fishes, other non-fish objects, or the background –Segmentation/grouping is a very difficult problem

37. 240-572: Chapter 1: Introduction 36 Steps in a Pattern Recognition System – cont. Image segmentation is one of the most difficult problems in computer vision –Face detection, for example, can be viewed as a image segmentation problem

38. 240-572: Chapter 1: Introduction 37 Steps in a Pattern Recognition System – cont In speech recognition, the segmentation problem is called source separation –Mixed speech signal –Separated signal source 1 –Separated signal source 2

39. 240-572: Chapter 1: Introduction 38 Steps in a Pattern Recognition System – cont. Feature extraction/selection –A critical step for pattern recognition –Seeking distinguishing features that are invariant to irrelevant transformations of the input –Biometrics can be viewed as a feature selection problem Classification Post-processing –Context information –Multiple classifiers

40. 240-572: Chapter 1: Introduction 39 The Design Cycle

41. 240-572: Chapter 1: Introduction 40 Learning Supervised learning –A category label is given for each pattern in a training set Unsupervised learning –The system forms clusters or natural groupings of the input patterns –The study of category formation Reinforcement learning –No desired output is provided; the feedback is given