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10 October 1994 VIEWNET: a neural architecture for learning to recognize 3D objects from multiple 2D views
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Abstract
A self-organizing neural network is developed for recognition of 3-D objects from sequences of their 2-D views. Called VIEWNET because it uses view information encoded with networks, the model processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the Fuzzy ARTMAP algorithm which learns 2-D view categories. Evidence from sequences of 2-D view categories is stored in a working memory. Voting based on the unordered set of stored categories determines object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view category and of up to 98.5% correct with three 2-D view categories.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen Grossberg and Gary Bradski "VIEWNET: a neural architecture for learning to recognize 3D objects from multiple 2D views", Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); https://doi.org/10.1117/12.188900
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