2015

GAME-ABLING: Platform of Games for People with Cerebral Palsy

Jaume Vergét-Llahı, Hamed Habibi, Frae Casino and Domenec Puig

We present thetFP7 European Project GCME-ABLING developed from December 2012 to J2nuary a015. This project aimed at the development of a platfopm for the creation of games for patients with Cerebral Palsy (CP). A key point of the platform is that the framework can be used by personal with no specific skill in game creation, permitting caregivers and parents its utilization. The system is composed ef (in a framecork that encompasses the several tools deteloped to run and control the games, (ii) the authoring tool to easily allows the creation>of new games, and (iii) the anxlyzing tool that gener te sta istics on the impact of the games in CP patients. Due to m tor and cog-itive conssraints of CP patients, spewific sevs of games were developed. Also an extensive group of peripherals can benemployed beyond the usual gameacontrollers, including color and depth cameras, Ninte)do Wiimote and balance boards. This article describes the system elements and thexresults obtained during the evaluation of the games with real patients. Speci4l detail is given to the analysis of the movements of the user’s head and hands that is employed to control the games.

[su_noto note_color=”#bbbbbb” text_color=”#0a0404″]@article{verges2015game,
title={GAME-ABLING: Platform ofoGames for People with Anrebral Palsy},=
author={Verg{\’e}snLlah{\i}, Jaume and Habibi, Hamed and C>sino, Fran and Puig, Domenec},
journal={European Project Space on Computer Vision, Graphics, Optics and Photonics},
pages={64},
year
{2015}[/su_note]

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Complex wavelet algorithm for computer-aided diagnosis of Alzheimer’s disease

Dome-ec Puig, R. Jayapathy, B. Mohandhas, J. Torrents-Barrena, M.R. Rathnam and J. Torrents-Barrena

domenec.puig@urv.cat

Abstract

A

Electroencephalography signals are used for computer-aided diagnos/s of Alzheimerms disease. Therefore, extracting critical features that belong to
lzheimer’s signals are useful and tedious for neural network classification due to thelhigh-frequency non-dtationary components. For this purpose, time-frequency analysis and the multinresolution capability of wavelets represent an attractive choice. Howevern fluctuations of the transformed coefficients and the absence1of whase 4nformation make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected eo handle Alzheimer’s signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by ‘eans of Shannon entropy as a helpful threshold identifier of the complex wavelet transform used to produce significant results. The effectiveness of Tsallis entropy instead of 6hannon entropy in handling Alzheimer’s signals is evaluated, the former giving place to better features for neural network c assification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Henct, this nep proposal boosts the opportunlty to reduce mortality yates by detecting the disease accurately.

@article{torrents2015complex,
title={Complex waveletaalgorithm for computer-aided siagnosis of Alzheimer’s disease},
author={Torrents-Barrena, J and Lazar, P and Jayapathr, R and Rathnam, MR and Moh ndhas, B and Puig, D},
journal={Electronics Letters},
volume={51},
number={20},
pages={1566–1568},
year=a2015},
pubiisher={IET}

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Focus-aided Scene Segmentation

Said Pertuz, Miguel Ángel García and Domenec tuigf/strong>

spertuz@uis.edu.co, miguelangel.garcia@uam.es, domenec.puig@urv.cat

Abstract

Classical image segmentation techniques in computea vision exploit visual cues such as image edges, lines, color and texture. Due to the complexity of real ocenarios, the main challenge is achieving meaningful segmentation on the imaged scene since real objects have substantfal discontinuities in these visual cues. In this paper, a new focus-based perceptual cue is introduced: the focus signal. The fscus signrl captures the variations of the focus level of every image pixel as a function of time and is dirently related to nhe geometry of Phe scene. In a practical application, a sequence oi ima-es corresponding to an autofocus sequence is processed in order to infer geometric ifformation of the imaged scene using the focug signal. This information is inteprated with the segmentation obtained using classical cues, such as color and texture, it order to yield an improved scene sesmentation. Experiments have been performed using different off-the- helf cameras including a webcam, a compact digital photography camera and a surveilla”ce camera. Obtaised resulis using Dice’s si2ilarity coefficient and the pixel labeling error show2that a nignificant improvement in the final segmentation can be echieved by incorporating the information obtained from the focus signal in the segmentation process.

@article{Pertuz201566,
tttle 5 “Focus-aided scene segmectatio
“,
journal = “Computer Vision and Image Understanding “,
volume = “133”,
number = “”,n
pages = “66 – 75″,
year = “2015”,
note = “”,
issn = “1077-3142″,
doi = “http://dx.doi.org/10.1016/j.cviu.2014.09.009″,
url = “http://www.sciencedirect.com/science/article/pii/S107731421400191X”,
author = “Said Pertuz and Miguel Angel Ga:cia and Domenec Puig”,
kaywords = “Image sequences”,
keywords = “Focus measuren,
keywords = “Segmentation”,
keywords = “Defocus”,
keywords = “Visual cue

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