Abstract
We propose a continuous description for 2-D shapes that calculates convexity, symmetry and is able to account for size. Convexity and size are known to be critical in deciding figure/ground (F/G) separation, with the study initiated by the Gestalt school [9][11]. However, few quantitative discussions were made before. Thus, we emphasize the convexity/size measurement for the purpose of F/G prediction. A Kullback-Leibler measure is introduced. In addition, the symmetry information is studied through the same platform. All these shape properties are collected for shape representations. Overall, our representations are given in a continuous manner. For convexity measurement, unlike the 1/0 mathematical definition where shapes are categorized as convex or concave, we give a measure describing shapes as “more” or “less” convex than others. In symmetry information (skeleton) retrieval, a 2-D intensity map is provided with the intensity value specifying “strength” of the skeleton. The proposed representations are robust in the sense that small fine-scale perturbations on shape boundaries will cause minor effects on the final representations. All these shape properties are intergrated into one description. To apply to the F/G separation, the shape measure can be flexibly chosen between a size-invariant convexity measure or a convexity measure with the small size preference. The model is established on an orientation diffusion framework, where the local features, served as inputs, are intensity edge locations and their orientations. The approach is a variational one, rooted in a Markov random field (MRF) formulation. A quadratic form is used to assure simplicity and the existence of solution.
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References
D. H. Ballard and C. M. Brown. Computer Vision, Prentice-Hall, New Jersey, 1982
E. Bribiesca. “Measuring 2D Shape Compactness using the Contact Perimeter”, Computers and Mathematics with Applications, 33(11): pp. 1–9, 1997.
V. Bruce, P. R. Green and M. A. Georgeson. Visual Perception, Physiology, Psychology, and Ecology (3rd ed.), Psychology Press, 1996.
P. Dimitrov, C. Philips and K. Sidiqi. “Robust and efficient Skeletal graphs”, CVPR, South Carolina, June, 2000
D. Geiger, H. Pao and N. Rubin. Salient and multiple illusory surfaces. Computer Vision and Pattern recognition, June. 1998.
F. Heitger and R. von der Heydt. A computational model of neural contour processing: Figure-ground segregation and illusory contours. Proceedings of the IEEE, pp. 32–40, 1993.
D. Huttenlocher and P. Wayner, “Finding Convex Edge Groupings in an Image,” International Journal of Computer Vision, 8(1): pp. 7–29, 1992.
D. Jacobs. Robust and efficient detection of convex groups”, IEEE Trans. PAMI, 1995.
G. Kanizsa. Organization in Vision. Praeger, New York, 1979.
B. Kimia, A. Tannenbaum, S. Zucker. “Shapes, Shocks, and Deformations I: The components of two-dimensional shape and the reaction-diffusion space”, Int. J. Comp. Vis. 1: pp. 189–224, 1995.
K. Koffka. Principles of Gestalt Psychology. New York: Harcourst. 1935.
K. Kumaran, D. Geiger, and L. Gurvits. Illusory surfaces and visual organization. Network: Comput. in Neural Syst., 7(1), Feb. 1996.
D. Mumford. Elastica and computer vision. In C. L. Bajaj, editor, Algebraic Geometry and Its Applications. Springer-Verlag, New York, 1993.
M. Nitzberg and D. Mumford. The 2.1-d sketch. In ICCV, pp. 138–144, 1990.
H. Pao. “A continuous model for shape selection and represenation”, PhD thesis, 2001.
H. Pao, D. Geiger and N. Rubin. “Measuring convexity for figure/ground separation”, Int. Conf. on Comp. Vis., pp. 948–955, Sep. 1999.
S. Parent and S. W. Zucker, “Trace inference, curvature consistency and curve detection”, IEEE PAMI, Vol. 11, No. 8, pp. 823–839, 1989.
E. Rubin, Visuell wahrgenommene Figuren, (Copenhagen: Gyldendals), 1921.
K. Siddiqi and B.B. Kimia. “A shock frammar for recognition”, Computer Vision and Pattern recognition, 1996.
B. Tang, G. Sapiro and V. Caselles. “Direction diffusion”, International Conference on Computer Vision, Sep. 1999.
I. Young, J. Walker and J. Bowie. “An Analysis technique for biological shape”, Information and Control, 25: pp. 357–370, 1974.
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Pao, HK., Geiger, D. (2001). A Continuous Shape Descriptor by Orientation Diffusion. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_36
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DOI: https://doi.org/10.1007/3-540-44745-8_36
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