Analyzing skewed symmetries

Symmetry is pervasive in both man-made objects and nature. Since symmetries project to skew symmetries, finding axes of skew symmetry is an important vision task. This paper presents a linear time algorithm for finding the axes of skew symmetry, where the degree of symmetry is known. First, we present a review and critique of current methods for finding the axes of skew symmetry. Next, we decompose the problem of finding skew symmetry into the subproblems of solving for the rotational parameter of a “shear symmetry” and recovering the shear parameter of a reflexive symmetry. Using this approach, the authors derive a direct, non-heuristic moment-based technique for finding the axes of skew symmetry. For skew symmetric figures with degree of symmetry less than five we obtain a closed-form solution. The method does not rely on continuous contours but assumes there is no occlusion and requires knowing the contour's degree of symmetry. It is the first algorithm to find the axes of skew symmetry inO(n) time, where n is the number of contour points. The method is especially suited to industrial applications where the degree of symmetry is often knowna priori. Examples of the method are presented for both real and synthetic images, and an error analysis of the method is given.

[1]  Hagit Hel-Or,et al.  Completion of occluded shapes using symmetry , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Ramakant Nevatia,et al.  Using Symmetries For Analysis Of Shape From Contour , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[3]  Azriel Rosenfeld,et al.  Axial representations of shape , 1986, Computer Vision Graphics and Image Processing.

[4]  Shiu Yin Yuen,et al.  Shape from Contour Using Symmetries , 1990, Alvey Vision Conference.

[5]  Philippe Saint-Marc,et al.  B-Spline Contour Representation and Symmetry Detection , 1990, ECCV.

[6]  Ari D. Gross Toward Object-Based Heuristics , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Hagit Hel-Or,et al.  A measure of symmetry based on shape similarity , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Takeo Kanade,et al.  Recovery of the Three-Dimensional Shape of an Object from a Single View , 1981, Artif. Intell..

[9]  R. Nevatia,et al.  Perceptual organization for computer vision , 1989 .

[10]  Radu Horaud,et al.  On the geometric interpretation of image contours , 1989 .

[11]  Stuart A. Friedberg,et al.  Finding axes of skewed symmetry , 1986, Comput. Vis. Graph. Image Process..

[12]  Bruno Buchberger,et al.  Applications of Gro¨bner bases in non-linear computational geometry , 1988 .

[13]  Ari David Gross Towards object-based heuristics , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Kent A. Stevens,et al.  The Visual Interpretation of Surface Contours , 1981, Artif. Intell..

[15]  Terrance E. Boult,et al.  SYMAN: a symmetry analyzer , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Ari D. Gross Shape constraints from parametric and non-parametric models , 1993 .

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

[18]  Bruno Buchberger,et al.  Applications of Gröbner Bases in Non-linear Computational Geometry , 1987, Trends in Computer Algebra.

[19]  Alan L. Yuille,et al.  An Extremum Principle for Shape from Contour , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Radu Horaud,et al.  On the Geometric Interpretation of Contours , 1988, Artif. Intell..

[21]  Yehezkel Yeshurun,et al.  Detection of interest points using symmetry , 1990, [1990] Proceedings Third International Conference on Computer Vision.