Globally Optimal Grouping for Symmetric Boundaries

Many natural and man-made structures have a boundary that shows certain level of bilateral symmetry, a property that has been used to solve many computer-vision tasks. In this paper, we present a new grouping method for detecting closed boundaries with symmetry. We first construct a new type of grouping token in the form of a symmetric trapezoid, with which we can flexibly incorporate various boundary and region information into a unified grouping cost function. Particularly, this grouping cost function integrates Gestalt laws of proximity, closure, and continuity, besides the desirable boundary symmetry. We then develop a graph algorithm to find the boundary that minimizes this grouping cost function in a globally optimal fashion. Finally, we test this method by some experiments on a set of natural and medical images.

[1]  Steven W. Zucker,et al.  Computing Contour Closure , 1996, ECCV.

[2]  Lance R. Williams,et al.  A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds , 1998, International Journal of Computer Vision.

[3]  Jun Wang,et al.  Salient closed boundary extraction with ratio contour , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Daniel P. Huttenlocher,et al.  Finding convex edge groupings in an image , 2004, International Journal of Computer Vision.

[5]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..

[6]  Alan L. Yuille,et al.  Segmenting by seeking the symmetry axis , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[7]  Ian H. Jermyn,et al.  Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Alan L. Yuille,et al.  FORMS: A flexible object recognition and modelling system , 1996, International Journal of Computer Vision.

[9]  H. Blum.  Biological shape and visual science (part I) , 1973 .

[10]  R. Nevatia,et al.  Perceptual Organization for Scene Segmentation and Description , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Song Wang,et al.  Convex grouping combining boundary and region information , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Gérard G. Medioni,et al.  Inferring global perceptual contours from local features , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Lance R. Williams,et al.  Segmentation of Multiple Salient Closed Contours from Real Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Shimon Ullman,et al.  Structural Saliency: The Detection Of Globally Salient Structures using A Locally Connected Network , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[15]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[16]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[17]  David W. Jacobs.  Robust and efficient detection of convex groups , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  A. Witkin,et al.  On the Role of Structure in Vision , 1983 .

[19]  H. Blum.  Biological shape and visual science. I. , 1973, Journal of theoretical biology.

[20]  M. Leyton.  Symmetry, Causality, Mind , 1999 .

[21]  M. Brady,et al.  Smoothed Local Symmetries and Their Implementation , 1984 .

[22]  Peter Kovesi,et al.  MATLAB Functions for Computer Vision and Image Analysis , 2004 .

[23]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[24]  Michael Lindenbaum,et al.  A Generic Grouping Algorithm and Its Quantitative Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  David W. Jacobs,et al.  Robust and Efficient Detection of Salient Convex Groups , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  James H. Elder,et al.  Contour Grouping with Prior Models , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Ronen Basri,et al.  Extracting Salient Curves from Images: An Analysis of the Saliency Network , 2004, International Journal of Computer Vision.

[28]  Kim L. Boyer,et al.  Quantitative measures of change based on feature organization: eigenvalues and eigenvectors , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.