Multidimensional Indexing For Recognizing Visual Shapes

Abstract

A homogeneous approach for acquisition, storage, and recognition of nonparametric shapes from images, using a novel shape representation based on shape autocorrelation operators is presented. A theoretical and experimental analysis of the computational complexity, recognition performance with increasing database size, and fault tolerance of the approach is presented. The system has been tested extensively with more than 300 arbitrary shapes in the database. Using a set of complex shapes, the recognition behavior with respect to occlusion, geometric transformation, and cluttered environments is studied. Unsupervised shape and subpart acquisition is demonstrated.<>