Some Recent Results in Structural Pattern Recognition
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1 Some Recent Results in Structural Pattern Recognition Roberto M. Cesar-Jr Computer Science Department IME - University of São Paulo - USP (FAPESP, CNPq, Capes/Cofecub)
2 Shapes Everywhere
3 The Map Image segmentation Graph matching Face Recognition Tracking Spatial relations Structural Pattern Recogntion
4 Shape Analysis Shape analysis has a long history in computer vision (e.g. Ledley, 1964) Well-stablished techniques for 2D shape recognition (OCR, biomedical shapes,...) State-of-art research: open problems! Beyond shape measures (area, curvature, etc)
5 Pattern recognition Statistical PR: feature vectors (Duda, Hart & Stork 2001; Theodoridis & Koutroumbas, 1999) Neural networks (Anderson, 1995; Hassoun, 1995; Kosko, 1992) Syntactic PR: primitives and grammars (Fu, 1982; Pavlidis 1977) Structural PR: parts and structure (Pavlidis 1977)
6 Structural pattern recognition
7 Structural Pattern Recognition Structure together with features Graph models Graph matching Optimization problems Applications: recognition of object parts Image segmentation object tracking
8 Inexact graph matching Model matching
9 Inexact graph matching Model
10 Inexact graph matching Model face Oversegmented face
11 Inexact graph matching Face graph Each region corresponds to a graph node. The arcs represent structural relations between regions.
12 Inexact graph matching Model graph Input graph
13 Inexact graph matching Inexact matching: homomorphisms
14 Inexact graph matching Attributed relational graphs Image objects (parts) Object feature vector G = ( V, E, µ,ν ) E = V V Structural relation between objects Relational feature vector
15 Inexact graph matching Attributed relational graphs G = ( V, E, µ,ν ) Average gray level and wavelet texture features Vector coordinates defined by corresponding centroids
16 Inexact graph matching f : V I V M G M : Model graph G I : Input graph
17 Inexact graph matching Possible solutions are cliques of the association graph between G I and G M.
18 Inexact graph matching Big problem: too many possible solutions! V M V I Objective function to be optimized in order to search a good solution Vertex cost Edge cost
19 Inexact graph matching Optmization algorithms: Gradient descent Beam search (tree search) Cliques EDAs (estimation of distribution) Genetic algorithms Bayesian networks
20 Inexact graph matching Optimization Algorithm
21 Inexact graph matching Tree search Genetic algorithms Bayesian networks
22 Inexact graph matching
23 Image segmentation User defined seeds
24 Image segmentation
25 Image segmentation
26 Image segmentation
27 Image segmentation
28 Image segmentation
29 Image segmentation
30 Tracking
31 Tracking Frame i-1 Frame i Frame i+1 Video sequences: graphs with temporal information! Important issues regarding the model.
32 Tracking
33 Tracking
34 Tracking
35 Tracking
36 Tracking
37 Tracking
38 Spatial relations
39 Spatial relations Spatial relations between objects Spatial reasoning and uncertainty Qualitative descriptions Well-known approaches for simple objects Things get complicated with complex shapes...
40 Spatial relations
41 Spatial relations Spatial relations: Above Along Below Between Directional (to the left, to the right, ) Surrounded
42 Spatial relations
43 Spatial relations: between
44 Spatial relations: between
45 Spatial relations X between Y and Z β = CH ( ) C C Y Z Y Z Y Z Y Z between Y Z Not between!
46 Spatial relations A ]a,b[ B Y Z Admissible segment Y Z
47 Spatial relations
48 Spatial relations Between: application in medical imaging Convex Hull Approach Visibility (admissible segments)
49 Spatial relations
50 Spatial relations
51 Spatial relations
52 Spatial relations
53 Spatial relations
54 Spatial relations
55 Spatial relations
56 With a little help from my friends Isabelle Bloch (ENST - Paris) Olivier Colliot (ENST - Paris) Endika Bengotxea, Pedro Larranaga (U. San Sebastian ) Luis A. Consularo (UNIMEP) Ana Beatriz V. Graciano (IME - USP) Celina M. Takemura (IME - USP) Financiamento: FAPESP, CNPq, CAPES/COFECUB
57 Concluding Remarks Plato's cave
58 Concluding Remarks
59 Concluding Remarks
60 Concluding Remarks
61 Concluding Remarks Get out of the matrix! :) Thank you!
62 References I. Bloch, O. Colliot, R. M. Cesar-Jr, IEEE Trans. Systems, Man and Cybernetics, R. M. Cesar-Jr. et al., Pattern Recognition, L. A. Consularo, R. M. Cesar-Jr., I. Bloch (em preparação). A. B. V. Graciano, Rastreamento estrutural de objetos, Dissertação de mestrado, IME-USP, 2006 (em preparação). A. Perchant and I. Bloch. Proc. IMTC 99, 16th IEEE Instrumentation and Measurement Technology Conference, C. M. Takemura, R. M. Cesar-Jr., I. Bloch, Proc. CIARP, LNCS, 2005.
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