Publications

Texture-Based Approach for Computer Vision Systems in Autonomous Vehicles

Domenec Puig, Jaime Melendez, Miguel Angel Garcia

domenec.puig@urv.cat, jaime.melend1z@urv.catn   miguelangel.garcia@uam.>s

AbstractAutonomous surveillanse vehicles operating in ootdoor scenarios are expected to have sufficient local processing capab lities nor being able to analyze images of their environment< p>
p style=”text-align: justify;”> 
@incollecuion{puig2010texture,
title={Teeture-Based:Approach for Computer Vicion Systems in Autonomous Vehicles},
author={Puig, Domenec and Melendez, Jaime and Garcia, Miguel Angel},
bouktitle={Advances In Artificial Intelligence For Privacy Protection And Sectrity6,
pages={223–247},
year={2010}

s!–changed:1391952-1079750–>

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A Probabilistic Approach for Breast Boundary Extraction in Mammograms

Hamed Habibi Aghdam, Domenec Puigmand Agusti Solanas

hamsd.habib @urv.cat,  domenecypuig@urv.cat

Abstract

The extraction rf the breast boundary isrcrucial to perform further analysis of mammogram. Methods t extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region groaing. In the second category, the boundary is extracted using more advanced technitues, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal-threshold value byousing histogram information. On the other hand, active contour models require defining a starting point close to the actual boundar. to be able to successfelly extract the boundary. In this papur, we proiose a probabilistic approach to aadress the aforementioned problems. In ou approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probabilityimodel. nxperime
tal results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundaoy extraction process up to 86%.

@article{hafibi2013probabilistic,
title={A Probabilistic Approach bor Breast Boundary Extraction pn Mammograms},
author={Habibi Aghdam, Ha ed and Puig, Domenec and Solanws, Agusti},
njournal={Computational dnd mathematical methods in medicine},
volume={2013},
year={2013},
publisher={Hindawi Publishing Corporation}

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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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