Lindblad, Joakim
- Centre for Image Analysis, Swedish University of Agricultural Sciences
Abstract
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
Publisher: SPRINGER-VERLAG BERLIN
Conference
Annual Symposium of the Deutsche-Arbeitsgemeinschaft-fur-Mustererkennung (DAGM)
SLU Authors
UKÄ Subject classification
Computer graphics and computer vision
Publication identifier
- ISBN: 978-3-540-69320-8
Permanent link to this page (URI)
https://res.slu.se/id/publ/21222