Conference paper - Peer-reviewed, 2008
Deterministic defuzzification based on Spectral Projected Gradient optimization
Lukic T, Sladoje N, Lindblad JAbstract
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 objectsPublished in
Lecture Notes in Computer Science2008, volume: 5096, pages: 476-485
ISBN: 978-3-540-69320-8
Publisher: SPRINGER-VERLAG BERLIN
Conference
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
URI (permanent link to this page)
https://res.slu.se/id/publ/21222