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
ISBN: 978-3-540-69320-8
Publisher: SPRINGER-VERLAG BERLIN

Conference

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

SLU Authors

• Lindblad, Joakim

  • Centre for Image Analysis, Swedish University of Agricultural Sciences
    

Permanent link to this page (URI)

https://res.slu.se/id/publ/21222