1. In: Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 30, Nr. 1 (2008), p. 36-51. Group of Adjacent Contour Segments for Object Detection 學生 : 戴玉書 教授 : 王聖智 老師 LEAR laboratory - INRIA Grenoble

2.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

3.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

4. Introduction Test image Training images

5.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

6. Contour segment network

7.  1. Edgels extracted with Berkeley boundary detector  2. Edgel-chains partitioned into straight contour segments  3. segments connected at edgel-chains’ endpoints and junctions Contour segment network [Ferrari et al. ECCV 2006]

8. - Links between edgel-chains

9. - Connects between segments

10. Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues [David R. Martin, Member, IEEE, Charless C. Fowlkes, and Jitendra Malik, Member, IEEE]

12.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

13. k adjacent segments (kAS) Three kinds of 2AS

14. - Descriptor of kAS 

15.  D(a, b) - Comparing kAS

16.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

17.  kAS can be detected by a depth-first search started from every segment Detecting kAS 1 2 34 5 6

18.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

19.  Using a clique-partitioning (CP) approach  Let G be a complete graph whose nodes are the training kAS, and arcs are weighted by d − D(a, b) kAS codebook Each resulting clique is a cluster of similar kAS 1 2 3 D(1, 2) D(1, 3) D(2, 3)

20. - Clique-partitioning (CP) approach 

21. The 35 most frequent 2AS types from the codebook The 35 most frequent 3AS from the codebook

22.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

23. |B| ・ |C| - dimensional window descriptor Object class detection -Training 1. Subdivide window into tiles 2. Compute a separate bag of PAS per tile 3. Concatenate these semi-local bags

24. - Training 1. Learn mean positive window dimensions 2. Determine number of tiles T 3. Collect positive example descriptors 4. Collect negative example descriptors: slide window over negative training images 5. Train a linear SVM

25.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

26. Object class detection -Testing 1. Slide window of aspect ratio, at multiple scales 2. SVM classify each window

27.  Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

28. Result

30. Red : 2AS Blue : HoG