DocumentCode :
2658248
Title :
Feature grouping and figure/ground discrimination: a recursive neural network approach
Author :
Hérault, Laurent ; Horaud, Radu
Author_Institution :
CEA-LETI, Grenoble, France
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
2606
Abstract :
The authors cast the feature grouping and figure/ground discrimination problems into a combinatorial optimization problem. The cost function for which a global minimum is to be sought is of the same mathematical structure as the energy function of a spin-glass system or of a recursive neural network. Hence, the global minimization problem can be solved by mean field annealing (MFA). The experimental results obtained with an MFA asynchronous implementation show that the method advocated is well suited to solve the problem
Keywords :
combinatorial mathematics; neural nets; optimisation; pattern recognition; picture processing; asynchronous implementation; combinatorial optimization; cost function; energy function; feature grouping; figure/ground discrimination; global minimum; mean field annealing; recursive neural network approach; spin-glass system; Computational modeling; Computer networks; Computer vision; Cost function; Image edge detection; Neural networks; Noise shaping; Psychology; Shape; Simulated annealing;
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.1991.170782
Filename :
170782
Link To Document :
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