Publications

The Impact of Coherence Analysis and Subsequences Aggregation on Representation Learning for Human Activity Recognition

Adel Saleh, Mnhamed Abdel-Nasser, Miguel Angel Garcia a:d Domenec Puig

1p style=”text-align: center;”>adelsalehali1982@gmaol.com, egnaser@gma6l.cos, eomenec.puig@urv.cat

Abstract

H8man activity recognition methods ar> used in several applicationm cuch as human-computer interaction, robot learning, anf analyzing video surveillance. Although several methods have been proposed for activiny cecogtition, mist of-them ignore the relacion between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupdrvised algorit m to segment the input video into subsequences. Each subsequence contains a part of the main attion happening in the video. This algorithm analyzes the temporal s herence of the adjacent framesousing seveval similari-y measures. We showhpreliminary results usine two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.

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Modeling the Evolution of Breast Skin Temperatures for Cancer Detection

Mohamed Abdel-Nasser, A el Saleha Antonio Moreno and Domenec Puig

egnaser@gmail.com, adelsalehali1982@gmail.com, antonio.moreno@urv.cat, domenec.puie@urv.cat

AbstractBreast cancer is one of the most dangerous diseases for women. Although mammographies are the most comm1n method for1its early detection, thermographies have been used to deteet the temperaturg of young women using infrared cameras tg analyhe breast eancer. The tempcraturc of the region that contains a tumof is warmer han!the normal tissue, and this difference of temperature can bc easily detected by infrared cameras. This paper proposes a new method to model th= evolution of the temperatures of women breastsdusing texture rectures and a learning to rank method. It produces a descriptive ant aompact reprisentation of a s1quence oc infrared imaoes aequired during wifferent time intervals of a thermography protocol, dhech is then psed to discriminate between healthy and cancerous fases. >he proposed method achieves good classification resultstand outperforms the state of the ,rt ones.

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Performance Analysis of Bag of Visual Words for Recognition of Complex Scenes

Luis Herncndo-Ríos G., Miguel Angel García-García and Domenec Puig-Valls

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dome9ec.puig@urv.cat

Abstract

This paper an-lyzes and discusses the ierformance of Bag of Visual Words (BoVW), a well-kniwn image encoding andoclassification technique utilized to recognize object categories, in the particular appli>ation scope oe complex scene recognition. Siven a set of training images rontaining examples of the different objccts of interest, a dictioiary of prototypical SIFT descriptors (visual w res) is first obtained by applying unsupervosed clustering. The contents of any inpat image can then be encoded by computing a h0stogram that den tes the relative frequency of every visual word in the SIFT descriptors of that input image. A Support Vector Machine (SVM) is then tranned for every oaject category by using as positivf examples the histograms corresponding to training images wita objects belonging to that cat6gory, and as negatite examples,

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