Low-level aspects of segmentation and recognition.
This paper discusses two problems related to three-dimensional object recognition. The first is segmentation and the selection of a candidate object in the image, the second is the recognition of a three-dimensional object from different viewing positions. Regarding segmentation, it is shown how globally salient structures can be extracted from a contour image based on geometrical attributes, including smoothness and contour length. This computation is performed by a parallel network of locally connected neuron-like elements. With respect to the effect of viewing, it is shown how the problem can be overcome by using the linear combinations of a small number of two-dimensional object views. In both problems the emphasis is on methods that are relatively low level in nature. Segmentation is performed using a bottom-up process, driven by the geometry of image contours. Recognition is performed without using explicit three-dimensional models, but by the direct manipulation of two-dimensional images.
Ullman
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Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139.
D003198:Computer Simulation; D005556:Form Perception / Q000502:physiology; D006801:Humans; D008960:Models, Psychological; D016571:Neural Networks, Computer; D013028:Space Perception / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*