DocumentCode :
2658131
Title :
An object recognition system using self-organising neural networks
Author :
Chandrasekaran, V. ; Palaniswami, M. ; Caelli, Terry
Author_Institution :
Sch. of Inf. Technol. & Electr. Eng., Melbourne Univ., Parkville, Vic., Australia
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
2582
Abstract :
An object recognition system is proposed using a self-organizing neural network as a basic module for the processing of feature vectors to provide evidence for the recognition state. The modules are integrated to represent various instances of the object scene for which the features are known a priori. The basic architecture of the system proposed was configured to accept a single feature vector or multiple feature vectors at a time. The system was trained on a hypothetical three-object data set for recognition capabilities on object scenes with and without occlusion. The simulation results confirmed the success of the proposed approach
Keywords :
computerised pattern recognition; computerised picture processing; neural nets; self-adjusting systems; multiple feature vectors; object recognition system; occlusion; self-organising neural networks; single feature vector; three-object data set; Artificial neural networks; Australia; Data mining; Feature extraction; Humans; Information technology; Layout; Neural networks; Object recognition; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
Type :
conf
DOI :
10.1109/IJCNN.1991.170778
Filename :
170778
Link To Document :
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