Object Recognition Using a Generalized Robust Invariant Feature and Gestalt's Law of Proximity and Similarity

In this paper, we propose a new context-based method for object recognition. We first introduce a neurophysiologically motivated visual part detector. We found that the optimal form of the visual part detector is a combination of a radial symmetry detector and a corner-like structure detector. A general context descriptor, named GRIF (Generalized-Robust Invariant Feature), is then proposed, which encodes edge orientation, edge density and hue information in a unified form. Finally, a context-based voting scheme is proposed. This proposed method is inspired by the function of the human visual system, called figure-ground discrimination. We use the proximity and similarity between features to support each other. The contextual feature descriptor and contextual voting method, which use contextual information, enhance the recognition performance enormously in severely cluttered environments.