A Continuous Model for Salient Shape Selection and Representation

We propose a new framework for shape representation and scenery shape selection. Various topics including figure/ground separation, shape axis construction, junction detection and illusory figure finding will be discussed. The model construction is inspired by the Gestalt studies. They suggest proximity, convexity, symmetry, etc, as cues for figure/ground separation and visual organization. By our distributed systems, we quantify those attributes for complete/partial shapes and use them for shape evaluations and representations. In particular, the shape convexity instead of other well-studied shape attributes such as the symmetry axis or size, will be emphasized. Two models are proposed. The decay diffusion process is applied in predicting figure/ground phenomenon, based on a convexity measure for figure/ground sharing the same area. The orientation diffusion process, adopting orientation information on shape boundaries/edges, will discuss the figure/ground separation or shape convexity comparison for regions not owning the same size. A Kullback-Leibler convexity measure is proposed, with a flexible scenario. Through a parameter, we are allowed to choose between a size-invariant convexity measure or one with small-size prefer-