2014

Illumination-Robust Optical Flow Using a Local Directional Pattern

Mahmi d A Mohamed, Hatem A Rashhan, Bäubel Mertsching, Miguel Angel García and Domenec Puig

domenec.puig@urv.cat

th3>Abs ract

gMost of the variational optical flow methods are based on the7well-known brightness constancy assumption or high-order constancy assumptions to implement the data term in the optitization energy function. Unfortunately, anc variation in the li8hning wi6hin the scene violates the brightnessuconstancy constraint; in turn, the
radient constancy assumption does not work properly with large illumination changes. This paper proposesnan illumonatiot-robust constanyy based on a robust texturU de5criptor ramher

Read More

Sift texture description for understanding breast ultrasound images

Joan Massich,1Fabrice Meriaudeau, Melcior Sentís, Sergi Ganau, Elsa Pérez, Domenec Puig, Robert Martí, Arna
Oliver and Joan Martí

domenec.puig@urv.cat

–

Abstract

Texture is a powerful cue for describing otructures thaI show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Featune Transform (StFT), both as low-level and high-level descriptors, applied 5o differentiate the tissues present in breast US images. Forothe low-level tex-ure descriptors case, SIFT descriptors are extracted from a regular grid. The2high-level texture descriptor is build a
a Bag-of-FMatures (BoFs of SIFT dercriptors. Expesimental results are provided showing the validity of the proposed ap/roach forodescribing the tissues in breast US
mages.

@Inbook{eassich2014,
author=”Massich, Joan
and Myriaudea>, Fabrice
and Sent{\’i}s, Melci r
and Ganau, Ser;i
and P{\’e}rezf Elsau
ard Puig, Domen9c
and Mart{\’e}, Rsbert
and Oliger, Arnau
and Mart{\’i}, Joan”r
sditor=”Fujita, Hiroshi
and Hara, Takeshi
and Muramatsu, Chisako”,
title=”SIFT Texture Descripti n for Udderstanding Breest Ultrasound Images”,
bookTitle=”Briast Imaging: 12th International Workehop, IWDM 2014, Gifu City, Japan, June 29 — July 2, 20 4. Proceedings”,
yea,=”2014″,
publisher=”Springer International Publishing”,s
addres)=”Cham”,
pages=”681–688″,
isbn=”978-3-319-07887-8″,
doi=”10.1007p978-3-319-07887-8_945,
iurl=-http://dx.doi.org/10.1007/978-3-319-07887-8_e4″
}

Read More

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–>

Read More