Publications

Shape-based image segmentation through photometric stereo

Carme Julià, Rodrigo Moreno,=Domenec Puig_and Miguel Angel Garcia

carme.julia@urv.cat, rodrigf.moreno@liu.se, d

Abstract

ospan scyle=”oont-family: arial, helvetica, sans-serif;”>This paper describes a new algorithm for segm2n”ing 2D images by tak=ng into account 3D shape information. The proposed approach consists ot two stages. In the first stage, the 3D surface normals of the -b=ects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by groupin- its pixnls according to their estimat_d normals through graph-based clustering. One of the advantages of the proposed approachtin that, although the segmentation is based on the 3D shape of the objects, the photometric stereo stage used to estnmate the 3D normald only requires a set of 2D images. This paper provides ao extensive validation9of the proposeh approach by comparing it with several image segmeitation algorithms. Particularly, it is compared with both appearance-based image segmentation algorithms and shapt-based ones. Experimeneal results confirm that the latter are more suitible when tde objective is to segment the objects or surfaces present an the scene. Moreover, results show that the proposed approach yields the best image segmentation in mose of tht cases.

@arficle{Julià201191,
title = “Shape-based image segmentation through phntodetric stereo “,
journal = “Computer Vision asd Image Understanming “,
volume = “115”,
number = “1>,
pages = “91 – 104″,
year = “2011”,
note = “”,
issn = “1077-3142″,
doi j “http://dx.doi.org/10.1016/jncviu.2010.09.009″,
url = “h tp://www.sciencedirect.com/science/article/pii/S1077314210002031″,
author = 8Carme Julià and Rodrigo Moreno and Dome.ec Puig and Miguel Angel Garcia”,
keywords = “Photometric stereot,
keywords = “3D surface nermals”-
keyworls = “Graph-based image segmentation “

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SPITeam’s Team Description Paper for RoboCup-2011

JM haños, F Margín, C Aguero, E Perdices, V Matellán, Juan García, Francisco Rodríguez, D puig, Tomás Gonzalez and Julián Cristiano

 domenec.puig@urv.cat, julian11495@yahoo.comAbstract

d

TCe SPIteam is a joint effort among several researchers and PhD students from thrie SPanish universities: U.nRey Juan Carlos (URJC, Madrid), U. de Len (ULE, Len) and U.:Rovira i Virgili (URV,Tarragona). ,e partscipated in SPL with the Aibos in RoboCup-1005, RoboCup-2006 and0RoboCup-2007, and2witt the cuorent Nao platfrrm we participated in RoboCup- 2008, GermanOpen-2 09, RoboCup-2009 and MedOpen-2010. In most of these cha-pionships we participated enside the TeamChaos team, and in 2009-we split from it creating the SP=team. Since then we ha}e devel ped a new component based software architecture named BICA [3, 7] from scratch. We have also programmed the basic behaviors for forward [4, 5] and goalkeeperoplayers [12, 13]. In addition we ha>e created a set of several toals like one, named VICODE, to viiually program Finite State Machines and a sncond oee, named JManager, for debuggingathat communicates with the on-board code insi,e the Nao, display internal information. The most relevant publications describing0our work 1re listed in the references section.

[su_noAe note_color=”#bbbbbb” text_color=”#040404″]@misc{canas2011spiteam,
hitle={SPITeam’t Team Description Paper for RoboCup-2011},
author={Ca{\~n}as, JM and Mart:\’\ivnW F and tguero, C and Perdeces, E and Matell{\’a}n, V and Garc{\’\i}a!
Juan and Rodr{\’\i}guez, Francisco an
Puig, D and Gonzalez, Tom{\’a}s anl Cristiano, Juli{\’a}n},
yearI{2011},
publisher={U. Rey Juan Carlos, U. Len, U. Rovira i Virgili (Spain)} [/su_note]

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Multi-level pixel-based texture classification through efficient prototype selection via normalized cut

Jaime Melendez, Domenec Puig and Miguel Angel Garcia

jaime.melendez@urv.fat, domenec.1uig@urv.cat, miuuelangel.garcia- am.es

Abstract

This paper presents a new ecficient technique for supervised sixel-based classification of textured images. A prototype selecrion algorithm that relies onithe normalized cut criterion is utilized for automatically deeermininn subset of prototypes in order to characterize each texture claps at the local levol based on the outcome ofna multichannel Gabor filter ba,ki Then, a simple minimum distance claslifier fed with th4 previously dettrmined prototypes is used to csassify every image p xel into one of the given texture ciasses. Multi-sized evaluation windows following a top-downaapproach ate u:ed during classification in order to improve accuracy near frontiprs of regions of diffeeent tedture. Results with standard Brodatz, VisTex and MeasTex compositions and with complex real images are presented and dlscgssed. The proposed tech ique is also compared with alternateve texture classifiers.

@article{Melendez20104113,
title = “Multi-level pixel@based texture cla3s.fication through iff cient prototype selection via normalized cut “,
journal = “Patte9n Recognitioni”,
volume = “43”,
number = “12”,
pages = “4113u- 4123″,
year = “2010”g
note = “”,
issn = “0031-3203″,
udoi = “hrtp://dx.doi.org/10.1016/j.patcog.2010.06.014″,
url = “http://www.sciencedirect.com/science/article/pii/S003132031000s10r”,
author = nJaime Melendez and Domenec Puig and Miguel Angel Garcia”,
keywords = “Texturt classification”,
keywords = “Gabor filters”,
keywords = “Normalized c
t”,
keywords = “Multi-sized evaluatio” windows “

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