1. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recognition Prof. George Papadourakis, Ph.D.

2. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recognition Systems Data Representation Feature Extraction X1X1 Classifier C1C1 Class X2X2 X1X1 X1X1 X2X2 X1X1 CμCμ General Automatic Classification Scheme Object

3. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 Data Representation (1/2)

4. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Data Representation (2/2) X(t) t0 t1t1 tntn t2t2 a. Time Seriesb. Geometric Object Pixel #1

5. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Raw Feature Vector Χ.  Defines a prototype in Ν-dimensional space.  Elements are random variables  General format Feature Vectors OR

6. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Two categories of prototypes.  C 1 : mandarins  C 2 : watermelons  Two measurements:  X 1 : Diameter  X 2 : Weight  Raw feature vector Χ=[X 1,X 2 ] T  2 separable groups are created Prototypes (1/2) X 2 :Weight Χ 1 :Diameter C1C1 C2C2

7. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Prototypes (2/2) X 2 :Weight Χ 1 :Diameter C1C1 C2C2  Two categories of prototypes.  C 1 : mandarins  C 3 : oranges  Two measurements:  X 1 : Diameter  X 2 : Weight  Non-Separable groups (overlapping)

8. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Separable Categories in 3D space (Ν=3)  Ν>3: difficult representation  Feature Vector Extraction: Dimensionality Reduction  Sets of features:  Intraset: Common Properties  Interset: Differences  Intraset are not useful for all sets Multidimensional Data

9. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory 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 find interset (separating) features between categories  Difficult from a raw input vector X  Solution: export of separable features from X  Example: Recogntion of printed characters. X = [X 1,X 2, …,X N ] T

10. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Feature Selection (2/2)  features:  x 1 : area of the right segment of the character  x 2 : area of the left segment of the character  x 3 : perimeter of the character  z 1 : total area to squared perimeter ratio  z 2 : symmetry degree  x 1,x 2,x 3 : independent variables, z 1,z 2 : dependent

11. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Define optimal decision procedures for recognition and classification  System Decision: which category a prototype belongs to  Classification of prototypes in Μ categories: C 1,C 2,…C M  Creation of decision boundaries to separate Μ areas of feature prototypes  Curves: Probability Density Functions Decision Boundaries (1/2)

12. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Decision Limit d(x 1,x 2 )=0 Two areas  d(x 1,x 2 )>0 category C 2  d(x 1,x 2 )<0 category C 1 Decision Boundaries (2/2) X1X1 X2X2 Probability Density Function for each category

13. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Conveyer Belt  Robot  Quality inspection  Assembly  Voice commands Application: Industrial Robot

14. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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) Application: Industrial Robot

15. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Application: Industrial Robot Vision Sensor Image Transformations Segmentation Feature Extraction Classification Step 1 Step 2 Step 3 } Image based object recognition

16. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Vision Sensor  sequence of photosensors (simple case)  camera (usual case)  Combination of cameras for stereoscopic vision and 3D analysis (complex cases)  Single camera solution: frame grabber  Input Data: digital image Application: Industrial Robot

17. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 suitable for the specific application Application: Industrial Robot

18. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 Application: Industrial Robot

19. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Feature Extraction Concave Perimeter Hole Area (Inner Area) Area (no holes) Biggest Dimension Diameter (Ferret) Perimeter Maximum Horizontal Chord

20. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Feature Extraction  The distinction between feature extraction and classification is 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 the application  Classification procedure is more general. Pattern Recognition Issues

21. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 Pattern Recognition Issues

22. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  Complexity of a model  Is it possible to construct a complex pattern recognition system, that makes perfect classification 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. Pattern Recognition Issues

23. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory Pattern Recognition Issues  How do we choose the most suitable model for a specific application;  How do we decide to reject one model;  Except through trial and error, is there a more systematic method of choosing a model;  Choosing the Classification Model

24. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 zero or the average of 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? Pattern Recognition Issues

25. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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 is difficult for some aspects  speech recognition. Pattern Recognition Issues

26. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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? Pattern Recognition Issues

27. Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Intelligent Systems Laboratory  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. References