A structural-description-based vision system for automatic object recognition

This paper presents the results of the integration of a proposed part-segmentation-based vision system. The first stage of this system extracts the contour of the object using a hybrid first-and second-order differential edge detector [23]. The object defined by its contour is then decomposed into its constituent parts using the part segmentation algorithm in [3]. These parts are then isolated and modeled with two-dimensional (2-D) superquadrics. The parameters of the models are obtained by the minimization of a best-fit cost function, The object is then represented by its structural description which is a set of data structures whose predicates represent the constituent parts of the object and whose arguments represent the spatial relationship between these parts, This representation allows the recognition of objects independently of their positions, orientations, or sizes. It is also insensitive to objects with partially missing parts, In this paper, examples illustrating acquired images of objects, the extraction of their contours, the isolation of the parts, and their fitting with 2-D superquadrics are reported, The recognition rate of the suggested vision system for simultaneously and randomly rotated, displaced, and resized objects is also reported, This framework can also be used for the compression of images, where only an ASCII file is transmitted describing the structural representation of the object. The reconstruction of objects from their structural description is illustrated and improvements are suggested.