An iterative parsing approach for contour fragments

This paper presents an approach for linking edge points into contour fragments, each of which is an ordered point sequence. In the problem of shape-based object detection, we investigate what characteristics of the contour fragments representation influence the detection performance. It is observed that if interest objects are represented by limited number of contour fragments, with moderate count of noisy points (not belonging to interest objects) brought in, the detection algorithm can reliably obtain interest objects. For this purpose, we propose to iteratively extract a pair of points which preserves the farthest geodesic distance in each connected component of edge map. We conduct experiments on the ETHZ dataset and compare with other typical methods for generating contour fragments. Experimental results demonstrate that the proposed approach outperforms some existing ones in object detection.

[1]  Gang Song,et al.  Untangling Cycles for Contour Grouping , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Andrew Blake,et al.  Multiscale Categorical Object Recognition Using Contour Fragments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Benjamin B. Kimia,et al.  3D curve sketch: Flexible curve-based stereo reconstruction and calibration , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  David A. McAllester,et al.  A Min-Cover Approach for Finding Salient Curves , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[5]  Rama Chellappa,et al.  Fast directional chamfer matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Joseph L. Mundy,et al.  Segregation of moving objects using elastic matching , 2004, Comput. Vis. Image Underst..

[7]  Benjamin B. Kimia,et al.  No Grouping Left Behind: From Edges to Curve Fragments , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Jianbo Shi,et al.  Contour cut: Identifying salient contours in images by solving a Hermitian eigenvalue problem , 2011, CVPR 2011.

[9]  Jianbo Shi,et al.  Many-to-one contour matching for describing and discriminating object shape , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yuehu Liu,et al.  A Voting Scheme for Partial Object Extraction under Cluttered Environment , 2013, Int. J. Pattern Recognit. Artif. Intell..

[12]  Cordelia Schmid,et al.  Bandit Algorithms for Tree Search , 2007, UAI.