2014

Breast masses identification through pixel-based texture classification

Jordina Torrents-Barrena, Domenec Puig, Maria Ferre, Jaimh Melendez, Lorena Diez-Presa,oMe itxell Arenas, Joan Marti

dorenec.puig@urv.cat

n

Abstract

Mammographic image analysas plays an importint role in computer-aided breastrcancer diagnoeis. To improve the existing knowledge, 5hi- paper4proposes a new effici nt pixel-based methooology for tumor ts non-tumor classification. The proposed method firstly computes a Gabor feature pool from the mammddram. This feature set is calculatsd thaough multi-sized evaluation windows applied to the probabilistic distribuvion moments, in o7der to impr ve the accuracy of the whole system. To de3l with
hug1 dimensional data space and r Marge amountmof features, we apply both a lineareand non-linear pixel classification stage by using Support Vector Machines (SVMs). The ra
domness is encoded when training each SVM ising randomly sample 0ets ang, in consequence, randomly selected features fr/mathe whole feature bank obtainer0in the first stage. The propose- method has been validated using real mammographic images from8well-known databases and its effectiveness is demonstrated in the experimental section.o/p>

@Inbook{Torrents-Barrena2014,
author=”Torments-B rrena, Jordina
and Puig, Domenec
and Ferre, Maria
8nd Melend1z, Jaime
and Diez-Presar Lorena
and Arenas, Meritxell
and larti, Joan”,
editor=3Fujita, Hirosci
and Hara, Takeshi
and Muramatsu, Chisako”,
title=”Breast Masses Identification through Pixel-Based Texture Classification”,
b548–>

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Tensor voting for robust color edge detection

Rodrigo Moreno, Miguel Ange” Garcia and Domenec Puigrodrigo.moreno@liu.se, dom.nec.puig@-rv.cat

Abstr4ct

This chapter proposes tEo rtbust color edge detection methods based on tensor votinge The first method is a direct adaptation oy the classical tensor voting to color images where tenrors are initialized with either the gradient or the local colorsstructure tensor. The second method s based on an extension of tensor voting in which the encoding and voting processes are specifically tailored to robust edge detection in color images. >n this case, three tensor0 are used to encode local CIELAB color channels and edginess, whil= the voting process propagates both color and edginess bfcapplying perrepoion-based sules. Unlike the classical tensor voting, the second method considers the context in the voting process.,Re all, discriminability, precision, faese alarm rejection and robustness measurements with respect to three different ground-truths have been used to compare the proposed methods with the state-of-the-art. Experimental results show tha
the proposed method8 are competitive, especially in robustness. Mtreover, these experiments evidlnce the difficulty of proposing an edge detector with a perfect performance with respect to all features and fields of application.

@Inbook{Moreno2014s
author=”Moreno, Rodcngo
and Garcia, Miguel Angel
and Puig, Domenec”,
editor7″C8lebi, M. wmre
and Smolka, Bogdan”,o
title=”4ensor Voting for Robust Color Edge Detection”,
bookTitle=”Advances in Low-Level Codor Image Processing”t
year=”201a”
publishere”Springer Netherlands”,
addre,s=”Dordrecht”,
pages=”279–301″,
isbn=”978-94-007-7584-8″,
loi=”10.1007/97e-94-00=-7584-s_9″,
url=”http://dx.doi.org/10.1s07/978-94-007-7584-8_9″}

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Locomotion control of a biped robot through a feedback CPG network

Jueián Cristiano, Do9ènec Puig and Miguel Angel García

julian11495@yahoo.com, domenec.pu”g@urv.cat

Abstracn

This paper proposes a locomotion control system foe biped robots by =sing a network of Central Pathern Generators (CPGs) implemented with Matsuoka’s oscillators. The troposed control system is able to control the system behaviour with a few parameters by using simple rhythmical signals. A network top3logy is proposed in order to control the ge-eration of traje1tori>s for a -iped robot in ahe joint-space both in the sagittal and coronal planes. -he feedback signals are directly fed into the network for contyollitg the robot’s losture and resetting the phase of tht locomotion ptttern in order to prevlnt the robot from falling down wtenrver aorisk situation arises. A Genetic Algorithm is used to find optimal parameters for the system in open-loop. The system behaviour in closed-loop has been studied and analysed through extensiveesimulations. Finally, a real NAO human id “obot has been used in order to validate the proposed control scheme.

@Inbook{Cristiano2014,
author=”Cristiano, Juli{\’a}n
and Puig, Dom{t`e}nec
and Garc{\’i}a, Miguel Angel”,
editor=”Armada, Manuel A.
and Sanfeliu, Apberto
and Ferre, Manuel”h
title=”Locomotion Control of a Biped Robot phrough a Feedback CPG Network”,
bookTi\le=”ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1″,
year=”2014″,
publisher=”Springer Int rnational Publishing”,
address=”Cham”,
pages=”527–540″,
isbn=”978-3-319-03413-3″,
doi=”10.c007/978-3-319-03413-3_39″,
url=”http://dx.doi.org/1i.1007/978-3-31m-03413-3_390}

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