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Deterministic Defuzzification based on Spectral Projected Gradient Optimization
University of Novi Sad, Faculty of Engineering.
University of Novi Sad, Faculty of Engineering.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2008 (English)In: 30th Symposium of the German Association for Pattern Recognition (DAGM), Berlin / Heidelberg: Springer , 2008, 476-485 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Berlin / Heidelberg: Springer , 2008. 476-485 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5096
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-86604DOI: 10.1007/978-3-540-69321-5_48ISBN: 978-3-540-69320-8 (print)OAI: oai:DiVA.org:uu-86604DiVA: diva2:126608
Available from: 2008-11-18 Created: 2008-11-18 Last updated: 2010-03-01Bibliographically approved

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Computer Vision and Robotics (Autonomous Systems)

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