1. Region labelling Giving a region a name

2. Image Processing and Computer Vision: 62 Introduction Region detection isolated regions Region description properties of regions Region labelling identity of regions

3. Image Processing and Computer Vision: 63 Contents Template matching Rigid Non-rigid templates Graphical methods Eigenimages Statistical matching Syntactical matching

4. Image Processing and Computer Vision: 64 Template matching Define a template a model of the object to be recognised Define a measure of similarity between template and similar sized image region

5. Image Processing and Computer Vision: 65 Measure dissimilarity between image f[i,j] and template g[i,j] Place template on image and compare corresponding intensities Need a measure of dissimilarity Last is best.... Similarity

6. Image Processing and Computer Vision: 66 Expanding If f and g fixed -  fg a good measure of mismatch  fg a good measure of match Compute match between template and image with cross-correlation

7. Image Processing and Computer Vision: 67 g is constant, f varies and so influences M Normalisation C is maximum where f and g are same. Limitations number of templates required rotation and size changes partial views

8. Image Processing and Computer Vision: 68 Template InputOutput

9. Image Processing and Computer Vision: 69 Flexible Templates Shapes are seldom constant Variation in shape itself in image of same shape viewpoint Non-rigid representations capture variability

10. Image Processing and Computer Vision: 610 Structure Flexible image structures Linked by virtual springs

11. Image Processing and Computer Vision: 611 Recognition Deform image structure To equate model and image Move image structures To colocate model and image Matching

12. Image Processing and Computer Vision: 612 Learning the model Accuracy of model determines success Model For each control point average, variance of location To be learnt with minimum external variation size, orientation, inconsistency of location

13. Image Processing and Computer Vision: 613 Parametric Models Parametrically define the shape straight line, circle, parabola, … Update parameters to match model and object

14. Image Processing and Computer Vision: 614 Example – Face tracking Eyes and mouth circles and parabolas locations, sizes, orientations Templates define image structures

15. Image Processing and Computer Vision: 615 Flexible templates, EigenImages Attempt to capture intrinsic variability of data Mathematical representation of variation

16. Image Processing and Computer Vision: 616 Take samples from a population plot values of parameters on a scatter diagram Mathematical Foundation

17. Image Processing and Computer Vision: 617 Rotate axes: one axis encodes most of information other axis encodes remainder Generalise to multiple dimensions

18. Image Processing and Computer Vision: 618 Images Use outline co-ordinates image values As the variables Normalise as much variability

19. Image Processing and Computer Vision: 619 Hand Eigenimages

20. Image Processing and Computer Vision: 620 Hand Gestures

21. Image Processing and Computer Vision: 621 Range of Eigenimages

22. Image Processing and Computer Vision: 622 Face Eigenimages

23. Image Processing and Computer Vision: 623 Recognition Retain n eigenvectors with largest eigenvalues Form dot product of these with image data Find nearest neighbour from training set

24. Image Processing and Computer Vision: 624 Statistical Classification Methods Derive characteristic feature measurements from image Form a feature vector that identifies object as belonging to a predefined class Need decision rules to make classification

25. Image Processing and Computer Vision: 625 Linear Discriminant Analysis Samples from different classes occupy different regions of feature space Can define a line separating them

26. Image Processing and Computer Vision: 626 Feature 1 Feature 2 Class A Class B

27. Image Processing and Computer Vision: 627 Decision d(X) = F 2 - mF 1 - c d(X) > 0 for points in class A d(X) = 0 for points on line d(X) < 0 for points in class B

28. Image Processing and Computer Vision: 628 height weight jockeys basketball players ? Nearest Neighbour Classifier Assign the new sample to the population whose centroid is closest.

29. Image Processing and Computer Vision: 629 Most Likely Incorporate range of possible class values

30. Image Processing and Computer Vision: 630 Take population variation into account Assume prior probability of observing class j is P(  j ) e.g. 10% of population are jockeys Assume a conditional probability distribution for each feature, x, of each population p(x|  j ). height weight jockeys basketball players ? Bayesian Classifiers

31. Image Processing and Computer Vision: 631 Multiply these curves by P(  j ) to give probability of a measurement belonging to each class. Divide by total probability of measuring x, to normalise. This gives the probability of the sample being from each class. x p p(x|  1 ) p(x|  2 )

32. Image Processing and Computer Vision: 632 Syntactic Recognition Objects’ structure (outline) can be described linguistically Primitive shape elements = words Grammatically correct sentences = a valid shape

33. Image Processing and Computer Vision: 633 Shape Grammar A set of pattern primitives terminal symbols A set of rules that define combinations of primitives (sentences) the grammar A start symbol represents a valid object Non-terminal symbols represent substructures in the shape

34. Image Processing and Computer Vision: 634 Recognition Grammar is generative Recognition is degenerative Recognition uses rules in reverse Terminal symbols are rewritten until a valid start symbol is attained

35. Image Processing and Computer Vision: 635 Chromosome Grammar

36. Image Processing and Computer Vision: 636 Chromosome Grammar

37. Image Processing and Computer Vision: 637 The Primitives

38. Image Processing and Computer Vision: 638 Example

39. Image Processing and Computer Vision: 639

40. Image Processing and Computer Vision: 640 Evaluation Classification rate Confusion matrix

41. Image Processing and Computer Vision: 641 Classification Rate How often does the classifier get the correct answer? Selection of training and test data must be carefully done.

42. Image Processing and Computer Vision: 642 Confusion matrix C(i,j) = number of times pattern i was recognised as class j. Want off-diagonal elements to be zero.

43. Image Processing and Computer Vision: 643 Summary Template matching Deformable templates Flexible templates Statistical classification