BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//LORIA - ECPv6.17.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.loria.fr
X-WR-CALDESC:Évènements pour LORIA
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20170326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20171029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20180325T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20181028T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20190331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20191027T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181030T140000
DTEND;TZID=Europe/Paris:20181030T160000
DTSTAMP:20181026T140216Z
CREATED:20181026T135222Z
LAST-MODIFIED:20181026T140216Z
UID:6147-1540908000-1540915200@www.loria.fr
SUMMARY:PhD Defense : Hayat Nasser
DESCRIPTION:Hayat Nasser will defend her thesis on Tuesday\, October 30th at 2pm in room C005. \nHer presentation is entitled « Tools for the analysis of noisy discrete curves ». \n \nDissertation committee:\n\n\n\n\n\nIsabelle DEBLED-RENNESSON\n\nProfesseure des Universités\n\nUniversité de Lorraine\n\nDirectrice de thèse\n\n\nFabien FESCHET\n\nProfesseur des Universités\n\nUniversité Clermont Auvergne\n\nRapporteur\n\n\nEric ANDRES\n\nProfesseur des Universités\n\nUniversité de Poitiers\n\nRapporteur\n\n\nYukiko KENMOCHI\n\nChargée de Recherche\n\nUniversité Paris-Est\n\nExaminatrice\n\n\nLaurent WENDLING\n\nProfesseur des Universités\n\nUniversité Paris Descartes (Paris V)\n\nExaminateur\n\n\nSalvatore-Antoine TABBONE\n\nProfesseur des Universités\n\nUniversité de Lorraine\n\nExaminateur\n\n\nPhuc NGO\n\nMaître de Conférences\n\nUniversité de Lorraine\n\nExaminatrice\n\n\n\n\nAbstract:\n\n\n\n\nIn this thesis\, we are interested in the study of noisy discrete curves that correspond to the contours of objects in images. We have proposed several tools to analyze them. The dominant points (points whose curvature estimation is locally maximal) play a very important role in pattern recognition and we have developed a non-heuristic\, fast and reliable method to detect them in a discrete curve. This method is an improvement of an existing method introduced by Nguyen et al.. The new method consists in calculating a measure of angle. We have also proposed two approaches for polygonal simplification: an automatic method minimizing\, and another fixing the vertex number of the resulting polygon.\n\nThen we proposed a new geometric tool\, called adaptive tangential cover ATC\, based on the detection of meaningful thickness introduced by Kerautret et al.. These thicknesses are calculated at each point of the contours allow to locally estimate the noise level. In this context our construction algorithm of adaptive tangential cover takes into account the different levels of noise present in the curve to be studied and does not require a parameter.\n\nTwo applications of ATC in image analysis are proposed: on the one hand the decomposition of the contours of a shape in an image into arcs and right segments and on the other hand\, within the framework of a project with an Indian university about the sign language and recognition of hand gestures. Firstly\, the method to decompose discrete curves into arcs and straight segments is based on two tools: dominant point detection using adaptive tangential cover and tangent space representation of the polygon issued from detected dominant points. The experiments demonstrate the robustness of the method w.r.t. noise. Secondly\, from the outlines of the hands extracted from images taken by a Kinect\, we propose several descriptors from the selected dominant points computed from the adaptive tangential cover. The proposed descriptors\, which are a combination of statistical descriptors and topological descriptors\, are effective and suitable for gesture recognition. \n\n\n\nKeywords: Discret geometry\, dominant points\, tangential cover\, polygonal simplification\, image processing
URL:https://www.loria.fr/event/phd-defense-hayat-nasser/
CATEGORIES:Soutenance
END:VEVENT
END:VCALENDAR