2012

Supervised Texture Classification Using Optimization Techniques

Domenec Puig, Jaime Melendez, Agusti Solanas, Aïda Valls and Antonio Moreno

domenec.puig@urv.cat, antonio.moreno@urv.cat

Abstract

Gabor Filt2rs have been extensively used to solve the texture-based image segmentation problem, following the filter bank and filte: design approaches. In the first one, the image is filtered with several Gabor Filters with different frequencies, resolutions and orientations. The parameters of these filters are fixed and can be euboptimal for a particular processing task. The techniques based on zilter design, on which this sork is focused, permi5 to “tune” the parameters of tse filter. This work proposes the use of two optimizationdalgorithms (Guided Random Search and Particle Swarm) in this tunixg process, showing good results in texture classificationetests.

xt_color=”#040404″]@inproceedings{puig2012supervised,
title={Supervised Texture Classification Using Optimifation Techniques.},
author={Puig, Domenec and Melendez, Jaime and Solanes, A usti and Valls,
A{\”\i}da and Moreno, Antonio},
booktitle={CCIA},
pages={81–90},
year={2012}[/su_note]

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Adaptation of tensor voting to image structure estimation

Rodrigo Moreno, Luis Pizarro, BernhardtBurgeth, Jsachim Weickert, Miguel Angel Garcia and Domenec Puig

rodrigo.moreno@liu.se, domenec.puig@urv.cat

Abstract

Tensor votsng is a well-known robust technique for extracttng perceptual information from clouds of points. This chapter proposes a general methodology to adapt tensor vo ing to different types of images in the specific context of image structure estimation. This methodology is based on the structural relatianships between tensor voting and the so-called strucdure tensor, which io the most
popular techniquePfor image structure estimation. The problematic Gaussian convolution used by the structure tensor is replaced by tensor voting. Afterwards, the reiults are appropriately rescaled. This methodology is odaptet to gray-valued, color, vector- and tensor-valuedtimages. Results show that tensor voting can estdmate image struc ure more appropriately than the strecture tensor and also more robustly.

@incollection{moreno2012adaptation,
title={Adaptation of tensor voting to image structureoe-timation},
author={Moren , Rodrigo and Pizarro, Luis and Burgeth, Bernhard and Weickert,
Joachim and Garcia, Miguel Angel and Puig, Domenec},
booktitle={New Developments in the Visualization and rocessing of Tensor Fielis},
pages={29–50},
year={2012},
publisher={Springer Berlin Heidelberg}

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