Recognition of objects is a particularly demanding problem, if one considers that each image must be interpreted in milliseconds (usually 30 or 40 frames/second). The problem becomes more difficult if the objects are distorted and/or partially occluded. In this case a sequence of local features are to be extracted, combined in a global shape description and classified as belonging to pre-defined sets of known shapes (reference shapes). In this paper we propose a massively parallel object recognition system, which makes use of the multi polygonal approximation scheme for the extraction of rotation and translation invariant shape features, in connection with artificial neural networks for the parallel classification of the extracted features. The system has been successfully applied for recognizing aircraft shapes in different sizes, orientations, with the addition of noise distortion and occlusion. Timings on the Connection Machine 200 are also reported

A Robust Neural Network Based Object Recognition System and Its SIMD Implementation

PETROSINO, Alfredo;SALVI, Giuseppe
1999

Abstract

Recognition of objects is a particularly demanding problem, if one considers that each image must be interpreted in milliseconds (usually 30 or 40 frames/second). The problem becomes more difficult if the objects are distorted and/or partially occluded. In this case a sequence of local features are to be extracted, combined in a global shape description and classified as belonging to pre-defined sets of known shapes (reference shapes). In this paper we propose a massively parallel object recognition system, which makes use of the multi polygonal approximation scheme for the extraction of rotation and translation invariant shape features, in connection with artificial neural networks for the parallel classification of the extracted features. The system has been successfully applied for recognizing aircraft shapes in different sizes, orientations, with the addition of noise distortion and occlusion. Timings on the Connection Machine 200 are also reported
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/32852
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