Figure-ground separation by a neural dynamical system

This paper describes a neural network inspired dynamical system approach to a perceptual grouping problem-figure-ground separation. In this approach, a non-linear differential equation is defined at each pixel site and coupled with those at its neighbours. The steady state solution would determine whether a pixel is part of a salient structure or background/noise. The neighbourhood couplings are used to achieve spatial interactions that are essential to perceptual grouping, such as excitation and inhibition. Experimental results on the grouping of dots in synthetic and real-world images demonstrate the efficacy of the proposed approach.

[1]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

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

[3]  Rosalind W. Picard,et al.  M-lattice: a novel non-linear dynamical system and its application to halftoning , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Radu Horaud,et al.  Figure-Ground Discrimination: A Combinatorial Optimization Approach , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  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.

[6]  C. Hwang,et al.  Diffusion for global optimization in R n , 1987 .

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

[8]  S. Geman,et al.  Diffusions for global optimizations , 1986 .

[9]  Narendra Ahuja,et al.  Extraction of early perceptual structure in dot patterns: Integrating region, boundary, and component gestalt , 1989, Comput. Vis. Graph. Image Process..

[10]  D. Jacobs Grouping for Recognition , 1989 .