Pattern Recognition

1. Pattern Recognition Prof. George Papadourakis, Ph.D.

2. Pattern Recognition Systems X1 C1 X1 X2 X2 Data Representation Feature Extraction Classifier Cμ X1 X1 Object Class General Automatic Classification Scheme

3. Data Representation (1/2) • Pattern Representation by Data Collection. • Patterns in time (time series) • Signal Sampling Ν time moments • Patterns in space (geometric objects) • Intensity values of the pixels from a digital image

4. Data Representation (2/2) Pixel #1 X(t) 0 tn t t1 t2 a. Time Series b. Geometric Object

5. Feature Vectors • Raw Feature Vector Χ. • Defines a prototype in Ν-dimensional space. • Elements are random variables • General format OR

6. Prototypes (1/2) • Two categories of prototypes. • C1: mandarins • C2: watermelons • Two measurements: • X1: Diameter • X2: Weight • Raw feature vector Χ=[X1,X2]T • 2 separable groups are created C2 X2:Weight C1 Χ1:Diameter

7. Prototypes (2/2) • Two categories of prototypes. • C1: mandarins • C3:oranges • Two measurements: • X1: Diameter • X2: Weight • Non-Separable groups (overlapping) C2 X2:Weight C1 Χ1:Diameter

8. Multidimensional Data • Separable Categories in 3D space (Ν=3) • Ν>3: difficult representation • Feature Vector Extraction:Dimensionality Reduction • Setsof features: • Intraset: Common Properties • Interset: Differences • Intrasetare not useful for all sets

9. Feature Selection (1/2) • Example: • Oranges – Intraset: weight, diameter, color • Mandarins– Intraset: weight, diameter, color • Mandarins - Oranges– Intraset: color • Mandarins - Oranges– Interset: weight, diameter • Goal: To findinterset (separating) features between categories • Difficultfrom a raw input vectorX • Solution: export ofseparable features fromX • Example: Recogntion of printed characters. X = [X1,X2, …,XN]T

10. Feature Selection (2/2) • features: • x1:area of the right segment of the character • x2:area of the left segment of the character • x3:perimeter of the character • z1: total area to squared perimeter ratio • z2: symmetry degree • x1,x2,x3: independent variables, z1,z2: dependent

11. X1 C2 C1 d(x1,x2)=0 X2 Decision Boundaries (1/2) • Define optimal decision proceduresforrecognition andclassification • System Decision:whichcategorya prototype belongs to • Classification of prototypes in Μ categories: C1,C2,…CM • Creation of decision boundariesto separate Μ areas of feature prototypes • Curves:ProbabilityDensityFunctions

12. X2 X1 Decision Boundaries (2/2) • Decision Limitd(x1,x2)=0 Two areas • d(x1,x2)>0categoryC2 • d(x1,x2)<0 categoryC1 • Probability Density Function for each category

13. TV monitor Image Processing Camera Robot Control Robot Arm Belt-Conveyer Application: Industrial Robot Pattern Recognition • Conveyer Belt • Robot • Quality inspection • Assembly • Voice commands

14. Application: Industrial Robot • Application Analysis – Pattern Recognition Problems: • Recognition of objects (shape based) • Camera view independency • Quality inspection • Voice commands recognition (from the supervisor) • Recognition of Supervisors ID (from speech). • Recognition of robots improper functioning (self test)

15. Application: Industrial Robot Vision Sensor } Step 1 Image Transformations Segmentation Feature Extraction Step 2 Classification Step 3 Image based object recognition

16. Application: Industrial Robot • Vision Sensor • sequence of photosensors (simple case) • camera (usual case) • Combination of camerasfor stereoscopic vision and 3D analysis (complex cases) • Single camera solution: frame grabber • Input Data:digital image

17. Application: Industrial Robot • Template Matching; • In some low budget applications (optical pattern recognition) • Images corrupted by noise • Object orientation not predefined. • Store big number of samples for every component (all the angles of view). • Digital image: 128x128 x 8 (color) = 16 Kbytes • Not suitablefor the specific application

18. Application: Industrial Robot • Feature Extraction • Image transformations • Enhancement - restoration procedures • Removal of components’ margins, • Color enhancement and equalization, • Edge Detection, • Removal of non-necessary information • Final outcome:binary representation of the component’s edges or borderlines • Segmentation • Separate image into meaningful regions Image Transformation & Segmentation, is Image Processing

19. Area (no holes) Feature Extraction Hole Area (Inner Area) Biggest Dimension Diameter (Ferret) Perimeter Concave Perimeter Maximum Horizontal Chord

20. Pattern Recognition Issues • Feature Extraction • The distinction between feature extractionandclassificationis somehow arbitrary. • An ideal feature extractor makes classification a simple procedure. • An ideal classifier don’t need a specialized feature extractor. • The distinction is more theoretical than practical. • Feature extraction depends on theapplication • Classification procedureis moregeneral.

21. Pattern Recognition Issues • Noise: • Error tolerance of the sensor (Related to basic physical processes at the molecular level) • Jitter • Distortion • Salt & Pepper noise • Electromagnetic interference • All practical applications are related to some form of noise at the data acquisition. • Integration of the noise source to the system

22. Pattern Recognition Issues • Complexity of a model • Is it possible to construct a complexpattern recognition system, that makes perfectclassification to training prototypes, but fails to classify real data? • Yes:it is possible a complex pattern recognition system to depend on some features of the training prototypes and not on the properties of real data. • Defining model complexity:a model not so simple to classify differences between categories and at the same time not so complex to fail in classifying real data.

23. Pattern Recognition Issues • How do we choose the mostsuitablemodelfor a specific application; • How do we decide to reject one model; • Except through trial and error, is there a moresystematicmethod of choosing a model; • Choosing the Classification Model

24. Pattern Recognition Issues • Missing values from feature vectors • Often, not all the values are available • How can a classifier function only with existing data? • The simple method is to regard all missing data as zeroor theaverageof the rest of the values, which possibly is not the best way • Similarly, we could have a lack of values at the training prototypes (creation of recognition system). • How can the classifier be trained?

25. Pattern Recognition Issues • Segmentation • Application of Industrial Robot: non overlapping components on the conveyer belt. What if they are? • The prototypes should be able to be segmented and recognized by the features of their segments. • Segmentation isdifficultfor some aspects • speech recognition.

26. Pattern Recognition Issues • Invariable Transoformations • Industrial Robot: object placing on the corridor • Orientation Translation & scaling independency • Complex transformations can do it, but only related to specific applications. • Optical Recognition of handwritten characters:classifier non-sensitive to line thickness • Image Recognition:light conditions, shadows. • Invariability • How do we predefine the presence of invariability? • How do we integrate such knowledge to the system?

27. References • Duda, Heart: Pattern Classification and Scene Analysis. J. Wiley & Sons, New York, 1982. (2nd edition 2000). • Fukunaga: Introduction to Statistical Pattern Recognition. Academic Press, 1990. • Bishop: Neural Networks for Pattern Recognition. Claredon Press, Oxford, 1997. • Schlesinger, Hlaváč: Ten lectures on statistical and structural pattern recognition. Kluwer Academic Publisher, 2002. • Satosi Watanabe Pattern Recognition: Human and Mechanical, Wiley, 1985 • E. Gose, R. Johnsonbaught, S. Jost, Pattern recognition and image analysis, Prentice Hall, 1996. • Sergios Thodoridis, Kostantinos Koutroumbas, Pattern recognition, Academiv Press, 1998.