Estimating object region from local contour configuration

In this paper, we explore ways to combine boundary information and region segmentation to estimate regions corresponding to foreground objects. Boundary information is used to generate an object likelihood image which encodes the likelihood that each pixel belongs to a foreground object. This is done by combining evidence gathered from a large number of boundary fragments on training images by exploiting the relation between local boundary shape and relative location of the corresponding object region in the image. A region segmentation is used to generate a likely segmentation that is consistent with the boundary fragments out of a set of multiple segmentations. A mutual information criterion is used for selecting a segmentation from a set of multiple segmentations. Object likelihood and region segmentation are combined to yield the final proposed object region(s).

[1]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[2]  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.

[3]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[4]  Frédéric Jurie,et al.  Groups of Adjacent Contour Segments for Object Detection , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Nava Rubin,et al.  Measuring convexity for figure/ground separation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jitendra Malik,et al.  Figure/Ground Assignment in Natural Images , 2006, ECCV.

[8]  Martial Hebert,et al.  Learning to Find Object Boundaries Using Motion Cues , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[9]  Jianbo Shi,et al.  Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach , 2008, ECCV.

[10]  Jörg-Stefan Praßni,et al.  A random walker based approach to combining multiple segmentations , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[12]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  Luc Van Gool,et al.  Real-time affine region tracking and coplanar grouping , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[14]  Luc Van Gool,et al.  Object Detection by Contour Segment Networks , 2006, ECCV.

[15]  Martial Hebert,et al.  Towards unsupervised whole-object segmentation: Combining automated matting with boundary detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Andrew Blake,et al.  Contour-based learning for object detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[17]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[18]  Jitendra Malik,et al.  Using contours to detect and localize junctions in natural images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Alexei A. Efros,et al.  Recovering Occlusion Boundaries from a Single Image , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[20]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[22]  Mariella Dimiccoli,et al.  Exploiting T-junctions for depth segregation in single images , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[23]  Takeo Kanade,et al.  A Hierarchical Markov Random Field Model for Figure-Ground Segregation , 2001, EMMCVPR.

[24]  B. Julesz,et al.  Figure-ground perception and random geometry , 1966 .

[25]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).