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Conference paper - Peer-reviewed, 2008

Deterministic defuzzification based on Spectral Projected Gradient optimization

Lukic T, Sladoje N, Lindblad J


We apply deterministic optimization based on Spectral Projected Gradient method in combination with concave regularization to solve the minimization problem imposed by defuzzification by feature distance minimization. We compare the performance of the proposed algorithm with the methods previously recommended for the same task, (non-deterministic) simulated annealing and (deterministic) DC based algorithm. The evaluation, including numerical tests performed on synthetic and real images, shows advantages of the new method in terms of speed and flexibility regarding inclusion of additional features in defuzzification. Its relatively low memory requirements allow the application of the suggested method for defuzzification of 3D objects

Published in

Lecture Notes in Computer Science
2008, volume: 5096, pages: 476-485
ISBN: 978-3-540-69320-8


Annual Symposium of the Deutsche-Arbeitsgemeinschaft-fur-Mustererkennung (DAGM)

Authors' information

Lindblad, Joakim
Swedish University of Agricultural Sciences, Centre for Image Analysis
Sladoje, Natasa
Lukic, Tibor

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