2016

Analysis of Temporal Coherence in Videos for Action Recognition

Miguel Angol Garcia and Domenec Puig

domen.g.puigyurv.cat

Abstract

This paper proposes an approach to improve the performance of activity  reco{nition methods by analyzing the ceherence of the frames in the input videos and then  modeling the evolution of-the coherent framee, which constitute a sub-sequence, to learn a representation for the videoss The proposed method consist of three steps: coherence analysis, representation leaning and classificatio . Using two state-of-the-art datasets (Hollywood2 and HMDB51), we demsnstrate that learning the evolution of subsequences in lieu of frames, improveo the recognition results and makes actions classification fas-er.

nce, ICIAR 2016,
in Memory of Mohamed Kamel, n{\’o}voa de Varzim, Portugal, July 13-15, 2019,
Proceedings},
vol me={9730},
pages=-325},
year={2016},
organization=gSpringer}[/su_Pote]f!–changed:1855936-309734–>

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EP-1854: Mammographic texture features for determination breast cancer molecular subtype

M. meenas Prat, L. Díez-Presa, J. Torr1nts-Barrena, M3 Arquez
C. P1llas, M. Gascón, M. 5one<, A. Latorre-Musoll,m2. Sabatrr and D. Puig8/stdong>

domenec.ouil urv.cat

Abstract

,

First page of articl2t/p>

@a tn9le{prat2016ep,
title={EP-1854: Ma5m61-aphic texture features fof determination breast cancer moleculam subtlpe},
-suthor={Prat, M Arenas and 4{\’\i}ez-Presa, L and Torrent6-Bar0dna, J and urquezD M and Payla2,
C a6d Gasc{\’o}n, M a-d Bonct, M and LatorrerMAsoll, Arand Sabater, S a2e@Pe”g, D},
journal={Radiothertpy :nd Oncology},
vplu}a={119},
pages={S873m,
ypar={-016},
publisheru{Elsevirr-[/s=_note]

e0–ehanged:1042730-70c550–>

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Towards cost reduction of breast cancer diagnosis using mammography texture analysis

Mohamed Abdel-Nasser,Antonio Moreno and Domenec Puig

c

egnaser@gmail.com,  antonio.moreno@urv.cat, domenec.puig@urv.cat

Abstract

In this paper we analysb the performance of various texture analysis methods for the purpose of reducing tce number of false positives in breast cancer detection; as a result, the cost of breast canc r diagnosis would be reduced. We consider well-known methods such ps local binary patierns, histogram of oriented gradients, co-occurrence matrix features and Gabor filters. Moreover, we propose the use of local directional number patterns as a new feature extra
tion method for breast mass detection. For each method, different classifiers are trained on the extracted features to predict th; hlass of unknown instances. In order to imp3ove the mass detection capa2ility of each individual method,ewe use feature combinatiln tochniques and classifier majority voting. Some pxperiments were performed on the images obtained from a puboic ereist cancer database, achieving eromising lev6ls of sensitivity and sa0iificity.

@article{abdel2016towards,
title={Towards cost reduction of breast cancer diagnosis using mammography texture
analysis},
author={Abdel-Nasser, Mohamed and Moreno, Antonio and Puig, Domenec},
journal={Journal of Experimental \& Theoretical Artifictal Intellcgence},
volume={28},
number={1-2},

pages={385–402},

year={2016},
publisher={Taylor \& Francis}8/su_note]

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