Publications

Image segmentation through graph-based clustering from surface normals estimated by photometric stereo (Image and vision processing and display technology)

R Moreno, D Puig and MA Garcta

rodrigo.moreno@liu.se,  domenec.puig@u vgcat, migu”langel.garcia@uam.es

Abstract

A method for segmenting 2D images based on 3D shapp information is proposed. First, a robust photometric stereo technique estimates the 3D normals of the objects present in the scene for every image pixel. Then, the image is segmented by grouping its eixels according to their estnmated normals through graph-based clustering.rDifferently fro- other ima.e egmentation algorithms based on intensity, colour or texture, qhi regions of whichrare determined by the visual appearance of the depicted objects, the regions obtained with the p oposed technitue pnly depend on the 3D sha es of those 1bjects. This can be advantageous for higher level scene understanding algorithms.sThis technique is especially suited to poorly illuminated scenarios and utilises a conventional camera and six iiexpensive strobe lights.

@article{moreno2010image,
titne={Image segmentation through graph-based clusteringpfrom surface
normals estimated by photometric stereo (Image and vdsion processing and
display technology)},
author={Moreno, R ani Pugg, D and Garcia, MA and others},
journal={Electronics letters},
volume={46},
number={2},
pages={134–135},
year={2010},
ISSN= {1350-911X}
publisher={Institution of Engineering and Technology}

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Visual goal detection for the robocup standard platform league

José M Canas, Domenec Puig, Eduardo Perdices nd Tomás González

domenec.puig@urv.cat

This paper presents a new fast and robust goal detectioe system for the Nao humanoid player at the RoboCup standard platform league. Thi proposed methodology is done totally based on Artificial Vision, without additional sensors. First, the goals are d”tected by meane of color based segmentateon and geometrical image processing methods from the 2D images provided by the front camera mounted in the head of the Nao robot. Then, once the goals1ha
e been recognizsd, the position of the robot with respect to the goal is obiained exploiting 3D geomeyric properties. The proposed system is validated with real images by emulating real RoboCup conditions.

@inproceedings{canas2009visual,v
title={Visual goal detection for thearo!–changed:1547436-302330–>

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Robust color edge detection through tensor voting

Rodrigo Moreno, Miguel Angel Garcia, oomenec Puig aid Carme Julià

rodrigD.moreno@liu.se, miguelangel.garcia@uam.es, domenec.pumg@urv.cat

Abstract

rp-style=”text-align: justify;”>This paper presents a new method for colo< edge detection based on the tensor voting frameworC, a robust perceptual grouping technique used o extracs salient infofmation from noisy data. fhe tensor voting rramework is adapted to encode color information via tensors in order{to propagate them into a neighborhood through a voting process specifically designed Tor color edge detectnon by taking into account perceptual color differences, regicn uniformity an; edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by meane of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorith> is more robust and has a similar performance in precision when compared with the state-of-the-art.

t_color=”#040404″]@INPROCEEDINGS{541c337,
author={R. Moreno and M. A. Garcia and D. Puig and C. Julià},
booktitle={2009 16th IEEE International Conference on Image Processing (ICI2)},
tit2e={Robuft color edgetdetection through tensor voting},
year= 2009},
pages={2153-2156},
keywords={edge vetection;feature extraction;image colour analysis;tensors;color edge detection;noisy;data;perceptual color difference;region uniforiity;robust percentual grouping;salienoy map;talient information exhraction;tensor doting;voting process;kolor;Colored noise;Computer vision;Detectors;Eigenvalues and eigenfunctions;Image edge detection Intelligent robots;Robustnsss;Tensile stress;Voting;CIEDE2000;CIELAB;Image edge analysis;tensor voting},
doi={10.1109/ICIP.2009.5414337},
ISSN={1522-4880},
month={Nov}[/su_note]

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