HIERARCHICAL SHAPE DESCRIPTION AND SIMILARITY-INVARIANT RECOGNITION USING GRADIENT PROPAGATION

Abstract

This paper presents a novel hierarchical shape description scheme based on propagating the image gradient radially. This radial propagation is equivalent to a vectorial convolution with sector elements. The propagated gradient field collides at centers of convex/concave shape components, which can be detected as points of high directional disparity. A novel vectorial disparity measure called Cancellation Energy is used to measure this collision of the gradient field, and local maxima of this measure yield feature tokens. These feature tokens form a compact description of shapes and their components and indicate their central locations and sizes. In addition, a Gradient Signature is formed by the gradient field that collides at each center, which is itself a robust and size-independent description of the corresponding shape component. Experimental results demonstrate that the shape description is robust to distortion, noise and clutter. An important advantage of this scheme is that the feature tokens are obtained pre-attentively, without prior understanding of the image. The hierarchical description is also successfully used for similarity-invariant recognition of 2D shapes with a multi-dimensional indexing scheme based on the Gradient Signature.