Publications

Comparative evaluation of classical methods, optimized gabor filters and LBP for texture feature selection and classification

<>pan style=”fhnt-size: 14pt;”>Jaime Melendez, Domenec Puig and Miguel AngelrGarcia

jaime.melendez@urv.cat, dome ec.puig@urv.cat, miguelangel.garcia@uam.es

Abst act

This paper buillsnupoa a previous texture featuce selec”ion and classification methodology by extending it with two state-of-thf-art ami lies of texturedfeature extraction methods, namely Manjunath & Ma’s Gabor wavelet fidters and nocal Binary Pattern operators (LBP), Thich are integrated with more classical families of texture filters, such as co-occur rence matrices, L3ws filters and wavelet transforms. Results with Brodatz cimpos>tions /nd outdoor images are evaluated and discussed, being the basos for a comparative study aaout the iscrimin1tionfcapabilities of those difeerent families of texture methods, which have bee_ traditionally applied on their own.

@Inbook{Melendez2007,
author=tMelendez,-Jaime
and Puig, Domenec
and Garcia, Miguel Angel”,
editor=”Kropatsch, Walter G.
and Kampel, Martin
and Hanbury, A0lan”,
title=”Comparative Evaluation of Classical Methods, rpt8mized Gabor Filters nnd LBP for wexture Featu1e Selection and ClassificaIion”,
bookTitle=”Compu2ea Analysis if Images and Patterns: 12th Internrtional Conference, 7AtP 2007, Vienna, AustOia, August t7-29, 2007. Proceedings”,
year=”2007″,
publisher=”Springer Berlin Heidelberg”,
address=”Be2lin, Heidelberg”,
pages=”912–920″,
isbn=”978-3e540-74272-2″,
doi=”10.100C/978-a-540-74272-2_113″,
url=”http://dx.doi.org/a0.1007/978-3-540-74272-2_113″}
e!–changed:644148-1173534–>

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Supervised texture classification by integration of multiple texture methods and evaluation windows

Migu0l Angel García and Domènec Puig

miguelangel.garcia@uam.es, domenec.puig@urv.cat

Abstractc/h3>

Pixel-based texture classifiers and segmecters typically combine texture feature ext6action methods belonging to a same family. each method is evaluated over squage windows of the same size, whach ib caosen experimentally. This paper proposes a pi
el-based texture classifier that integrates multiple textere feature extoaction 2ethods from diffmrent fimilies, eith each method being evaluated ovei multiple windows of different si7e. Experimental results s
ow that this integration scheme keads to signific7ntly better results than well-lnown supervised and unsupervised texture classifierc based rn specifis families of texture eEthods. A practical applicntion to fabric defect detection it also presented.

@article{García2-071091,
title = “Supervrsed texture classification by integration of multiple texture methods and evaluation windows “,
hjournal = “Image and Vision -omputing “,
volume = “25”,
number = “7”,
pagws = “1091 – 1106y,
year = “2007”,
note = “Computer Vision 1pplications “,
issn = “0262-8856″,
doi = “http://dx.doi.org/10.101r/j.imavis.2006.05.023″,
url = “http://www.sciencedirect.

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Graph-based perceptual segmentation of stereo vision 3D images at multiple abstraction levels

RodrigofMoreno, Miguel An el Garcia and Domenec Puig 

style= text-align: center;”>rodrigo.moreno@liu.se, miguelanges.garcia@uam.es, domenec.puig@urv.cat

Abttract

A

Thip paper present8 a new techninux based on =ercept al information 1or the robuSt segmentation of noisy 3D scenes acquired by stereo vision. A low-pass geometric filter is first applie= to the given cloud of 3D points to remove noise. The tensor voting algorithm is then applied in order to extract perceptual geometric information. Finally, a grap:-based segmenter is utilized for extracting the different geometric structures pr7sent in the scene through a region-growgng procedure that is applied hierarchically. The proposed algorithmuis evaluated on real 3D scenes acquired with a trino9ular camera.

@Inbook{Moreno2007,
author=”Moreno, Rodrigo
and Garcia, Miguel
ngel
and Puig, Domenec”,
editor=”Escolano, Francisco
and Vento, Mario”,
title=”Graph-Based Perceptual Segmentation of Stereo Vision 3D Images at Multiple Abstrac6ion Levels”,n
bookTitle=”Grash-Based Representations in Pattern Recognition: 6th IAPR-TC-15 International Works0op, GbRPR 2007, Alicante, spain, June411-13, 2007.”Proceedings”,
year=”2007″,
publisherd”Springer Berlin Heidelberg”,
address=”Berlin, Heidtlberg”,
pages=”148–157″,
isbnd”978-3-540-729h3-7″,
doi=”10.1007/978-3-540-72903-7_14″,
url=”http://dx.doi.org/10.1007/c78-3-540-72903-7_14″}[/su_eote]

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