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Tianyang Ma and Longin Jan Latecki Temple University

From Partial Shape Matching through Local Deformation to Robust Global Shape Similarity for Object Detection. Tianyang Ma and Longin Jan Latecki Temple University. Outline Contour. Contour is robust to changes in. illumination. texture. Contour-based object detection.

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Tianyang Ma and Longin Jan Latecki Temple University

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  1. From Partial Shape Matching through Local Deformation to Robust Global Shape Similarity for Object Detection Tianyang Ma and Longin Jan Latecki Temple University

  2. Outline Contour Contour is robust to changes in illumination texture

  3. Contour-based object detection [V. Ferrari, F. Jurie, and C. Schmid. IJCV 2009] [Q. Zhu, L. Wang, Y. Wu and J. Shi. ECCV 2008] [X. Bai, X. Wang, L. J. Latecki, W. Liu and Z. Tu. ICCV 2009] [C. Lu, L. J. Latecki, N. Adluru, X. Yang, and H. Ling. ICCV 2009] [S. Riemenschneider, M. Donoser, and H. Bishof. ECCV 2010] [P. Srinivasan, Q. Zhu, and J. Shi. CVPR 2010] [A. Toshev, B. Taskar, and K. Daniilidis. CVPR 2010]

  4. Challenges 1 . The contour of the desired object is typically fragmented over several pieces. This has been addressed by other approaches. 2 . Part of the true contour of the target object may be wrongly connected to part of a background contour resulting in a single edge fragment part to part matching needed

  5. Partial Shape Matching [S. Riemenschneider, M. Donoser, and H. Bishof. ECCV 2010] • The key ideas: • each contour part is represented as a submatrix • efficient matching with integral image We utilize these ideas but use different geometric features. Our object detection framework is very different.

  6. Outline • Shape Representation • Partial Matching in Images • Contour Selection as Maximal Clique Computation • Experiments

  7. Outline • Shape Representation • Partial Matching in Image • Contour Selection as Maximal Clique Computation • Experiments

  8. Shape Representation: Histogramless Shape Context Shape Context [Belongie et al. 2002] Distance Matrix: Angle Matrix: Histogram

  9. Contour part = dist. submatrix + angle submatrix Single partial contour P two matrices are used to represent the entire contour two block diagonal matrices represent the green contour part

  10. Relation of any two contour parts is submatrices Partial Contours P and Q two submatrices are used to represent spatial configuration of a part composed of two contour segments.

  11. Shape Similarity

  12. Shape Similarity

  13. Outline • Shape Representation • Partial Matching in Image • Contour Selection as Maximal Clique Computation • Experiments

  14. Partial matching between edge fragments and model contour Partial Model fragment Partial image edge fragment Partial matching determines: • Key advantages: • no model decomposition into parts is needed • no breaking or connecting edge fragments is needed •  tolerates missing contour fragments in edge image.

  15. Partial matching between edge fragments and model contour • 1) Construct a 4D tensor • 2) Take the maximum of the 4D matrix along different length , and suppress it to k – index of edge contour i - start point on model contour Partial matching: j - start point on edge contour k

  16. All relevant edge fragments are mapped to their corresponding model fragments. • Key benefits: • Top down selection of relevantedge subfragments. • All selected edge subfragmentsare mapped to 1D curve of the model contour.

  17. Problem formulation ? How to find the true contours of the target shape in the edge image? Key idea: Given a minimal required coverage of the model contour, we want to select non overlapping model fragments that maximize the configuration similarity to the corresponding image fragments.

  18. Outline • Shape Representation • Partial Matching in Image • Contour Selection as Maximal Clique Computation • Experiments

  19. Construction of Affinity Matrix The weighted affinity graph is denoted as G = (V, A). Each vertex of the graph corresponds to a partial match The affinity between and is

  20. Construction of Affinity Matrix High affinity: Low affinity:

  21. Problem with Affinity Matrix Wrong matches may also have high affinity:

  22. Detected Contours are Maximal Cliques A maximal clique is a subset of V with maximal average affinity between all pairs of its vertices. [ M. Pavan and M. Pelillo. PAMI 2007] In this example, the maximal clique has 4 nodes selected from over 500 nodes. Therefore, most clustering based approach may not succeed.

  23. Computing Maximal Cliques Indicator = selected maximal clique of vertices of V. In order to solve this combinational problem, we relax it to A vertex is selected as belonging to a maximal clique iff Each maximal clique corresponds to a local maximum of: We employ the new algorithm to find all significant local maxima: [H. Liu , L. J. Latecki, and S. Yan. NIPS 2010] Each local solution is not a final solution but a detection hypothesis. Furthermore, we locally deform the model, then evaluate and score each detection hypothesis.

  24. Outline • Shape Representation • Partial Matching in Image • Contour Selection as Maximal Clique Computation • Experiments

  25. Experiments on ETHZ dataset

  26. Quantitative Evaluation Interpolated Average Precision : AP

  27. Accuracy of boundary localization Each entry is the coverage/precision for correct detections at 0.4 FPPI.

  28. Thanks!

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