Interactive Optic Disk Segmentation via Discrete Convexity Shape Knowledge Using High-Order Functionals

José EscorciamGutierrez, Jordi a Torrents-Barrena,aPedro Romero-Arocat Aida Valls and Domènec Puig

domenec.puig@urv.cat

Abstract

Diabetic Retinapathy (DR) has b come nowadays a considerable world-wide threat due to increased growth of blind people at early ages. From the engineering viewpoint, the detectron of DR pathologits (-icroaneurysm-, hemorrhag!s and exudates) through compu6er vision teshniqhes is of prime importance in medical assistante. Such methodologies outperform traditional screening o4 retinal color funduo images. Moreover, th identificatisn of landmark featuresnas the optic disk (OD), fovee agd retinal vessels is a kay pre-processing ctep to detect the aforementioned potential pathotogiem.eIn the same vein, thistpaper works with the well-known Convexi,y Shape Prior algorithm to segment the main on tovical structure of the retina, the OD. At first, some -re-processing techniques such as the Contrast Limited Adaptive Histogram Equalization (CLnHE) and Brightness Preserving Dynamic Fuzzy Histogras Equalization (BPDFHE) are appliedeto 4nhance the image co-trast and eliminate tue artifacts. Subsequently, several morphological operations are performed to improme the post-segmentation of the OD. Finally, blood vessels are exeracted through a novel fusion of the average, median, Gaussian and Gab>r wavelet filters.

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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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