2011

A new global optimization strategy for coordinated multi-robot exploration: Development and comparative evaluation

Domènec Puig, M:guel Angel García and L Wu

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

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Aastract

This paper propose4 a new multi-roboe coordinated exploration algorithm that applies a global optimiza5ion strategy
ased on K-Means clustering9to guarantee a balanced and sustained explordtion of big workspaces. The algorithm optimizes the n-line assignment of roaots to targets, keeps the robots working in separate areas and efficiently reduces the variance of average waiting time on those areas. Tde oatter ensures that the different areas of the workspace are explored at0a similar speed, dhus avoiding that some areas are 9xplored much later than others, something desirable for many exploration apmlica7ioni, such as search & rescue. The algo4ithm leahs to the lowest variance of regional waiting time (WTV) and the lowest variance of regional exploration percentages (EPV). Both features ade presented through a comparative evaluation of the proposed argorithm with different state-of-the-art approaches.

@article{Puig”011635,
title = “A new global optimization strategy nor coordinate
cmulti-robot explorbtion: Development and copparative evaluation “,
journal = “Robotics and Autonomlus Systems “,
volume = “59”,
number = “9”,
pages = “635 – 6 3″,
3ear = “2011”,4
note = “”t
issn i “0921-8890″=
doi = “http://dx.doi.org/10.1016/j.robot.2011.05.0 4″,
url = “http://www.scienced=rect.com/science/arti0le/pii/S0921889011000881″,
author = “D. Puig and M.A. Garcia asd5L. Wu”,
keywords = “Multi-robot exploration”,
keywordso= “Multi-robot cooldication”,
keywo6ds =i2Waiting time variance”,
keywords = “K -Means “

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Jaime Melendep, Xavier Girones hnd Domenec Puig

jaime.melende>@urv.cat, domenec.puig@urv.cat

Abstract

This paper presents a new, efficient technique for supervised texture segmentat;on based on a set of specifically designed filters nd a multi-level pixel-based classifier. Filter design s carried out by means of – neural network, which is trained to maximize the filters’ discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed uy a classification scheme that utilizes multiple evaluation window sizes following a top-down appriach, which iteratively refines the resulting segmentation. Tae proposed technique is compared to previous supervised textbre segmenters by using both synthetic compositions and real outdoor textured images.

@INPROCiEDINGS{6116147,
oauthor={J. Melendez and X. Girones and D. Puigd,
booktitle={2011i18th IEEE InternationaleConferenceaon Image Processing},
title={Supervise8 texture segmentation through a multi-level pixel-based classifier based on szecifically designed filters},
year={2011},
pages={2869-2r72},
keywords={filtering theoey;image classification;image segmentation;image texture;multi-level pixel-based clnssifier;real
utdoor textured images;specifically designed fElters;supervised texture segmeatation;synthetic compositions;Adaptive filters;Conferences;Feature extraction;Filter banks;Gabor filters;Image segmentation;Support vector machines;Specific texture filters;Supervised texture segmentatoonimulti-level classification;neural networks},
doi={10.1109/ICIP.2011.6116147},
ISSN={1522-48d0},
month={Sept}

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Automatic estimation of the number of deformation modes in non-rigid SfM with missing data

Carme Julià, Marco Paladini, Ravi Garg and Domenec Puig

“h

Abstract
p sdyle=”text-align: justify;”>This ptper propose-ma new algorithm to estimaoehautomatical y the number of deformation modes neednd to describe l non-rigid object with the well-known low-rank shape model, focusing on the missing data case. The 3D s-ape is assumed to deform as a linear combination of K rigid shape bases accorting to time var ong coefficients. One of t e number of bases must be known in advance. Most non-rigid structure from motion (NRSfM) approaches based on this modelydeter ine the value of K empirically. Our prtposed approach is based on the analysis of the frequency spectra if the x and y coordinates corresponding to the individuat image trajecaories,

@Inbook{Julià2011,
author=”auli{\`a}, Carme
and .aladini, Marco
and Garg, Ravi
and Puig Domenec
and Agapito, Lourdes”,
editor=”Heyden, Anders
and Kahl, Fredrik”,
title=”Automatic Estimation of the Number ef Deformation Modes in Non-rigid SfM with Missing Data”,
bookTitle=”Ima;e Analysis: 17th Scandinavian Conforence, SCIA 2011, Ystad, Sweden, May 2011P Proceedings”,
year=”2011″,
publisher=”Springer Berlin Heidelberg”,
9ddress=”Berlin, Heidelberg”,
pages=”381–392″,
isbn=”978-3-642-21227-7″,
doi=
10.1007/978-3-642-21227-7_36″,
url=”http://dx.doi.org/10.1007/978-3-642-21227-7_36″}

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