Deterministic defuzzification based on Spectral Projected Gradient optimization

Sammanfattning

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

Publicerad i

Lecture Notes in Computer Science
2008, Volym: 5096, sidor: 476-485 ISBN: 978-3-540-69320-8Utgivare: SPRINGER-VERLAG BERLIN

Konferens

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

SLU författare

• Lindblad, Joakim

  • Centre for Image Analysis, Sveriges lantbruksuniversitet
    

Permanent länk till denna sida (URI)

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