Efficient Closed-Form Solution to Generalized Boundary Detection

Boundary detection is essential for a variety of computer vision tasks such as segmentation and recognition. We propose a unified formulation for boundary detection, with closed-form solution, which is applicable to the localization of different types of boundaries, such as intensity edges and occlusion boundaries from video and RGB-D cameras. Our algorithm simultaneously combines low- and mid-level image representations, in a single eigenvalue problem, and we solve over an infinite set of putative boundary orientations. Moreover, our method achieves state of the art results at a significantly lower computational cost than current methods. We also propose a novel method for soft-segmentation that can be used in conjunction with our boundary detection algorithm and improve its accuracy at a negligible extra computational cost.

[1]  Takeo Kanade,et al.  Image Understanding Research at CMU , 1982 .

[2]  Aldo Cumani,et al.  Edge detection in multispectral images , 1991, CVGIP Graph. Model. Image Process..

[3]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

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

[5]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[6]  Peter Meer,et al.  Edge Detection with Embedded Confidence , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Azriel Rosenfeld,et al.  Picture Processing and Psychopictorics , 1970 .

[8]  Jitendra Malik,et al.  Large displacement optical flow , 2009, CVPR.

[9]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  Alan L. Yuille,et al.  Occlusion Boundary Detection Using Pseudo-depth , 2010, ECCV.

[11]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Josef Kittler,et al.  Optimal Edge Detectors for Ramp Edges , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Jitendra Malik,et al.  Occlusion boundary detection and figure/ground assignment from optical flow , 2011, CVPR 2011.

[14]  M. Abidi,et al.  Detection and classification of edges in color images , 2005, IEEE Signal Processing Magazine.

[15]  Kurt Keutzer,et al.  Efficient, high-quality image contour detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Martial Hebert,et al.  Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation , 2008, ECCV.

[17]  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.

[18]  Martial Hebert,et al.  Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning , 2009, International Journal of Computer Vision.

[19]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Subhransu Maji,et al.  Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.

[21]  Hiroshi Murase,et al.  Parametric Feature Detection , 1996, International Journal of Computer Vision.

[22]  B. S. Manjunath,et al.  Probabilistic occlusion boundary detection on spatio-temporal lattices , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[24]  Xiaofeng Ren,et al.  Multi-scale Improves Boundary Detection in Natural Images , 2008, ECCV.

[25]  Cristian Sminchisescu,et al.  Generalized Boundaries from Multiple Image Interpretations , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[27]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[29]  Cristian Sminchisescu,et al.  Constrained parametric min-cuts for automatic object segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Carlo Tomasi,et al.  Edge, Junction, and Corner Detection Using Color Distributions , 2001, IEEE Trans. Pattern Anal. Mach. Intell..