[Jobinfo] Fwd: Fully funded PhD position in Computer Graphics
Mon May 23 09:54:59 CEST 2011
-------- Original Message --------
Subject: Fully funded PhD position in Computer Graphics
Date: Sun, 22 May 2011 22:21:23 +0200
From: Hyewon Seo <seo@unistra.fr>
To: wp@cg.tuwien.ac.at
Dear Professor Purgathofer,
We are seeking for applicants for a PhD position in Computer Graphics
at the University of Strasbourg, France. This position is fully funded
(the monthly salary is about 1500 euros) and will start in September
2011. Applicants must have completed a Master degree and should send
their application materials to: seo@unistra.fr. The position is opened
until filled. Please see below for full details of this PhD position.
We would be grateful if you could forward this mail to the students
who may be interested.
Best regards,
Hyewon
--
Dr. Hyewon Seo
CNRS - LSIIT, University of Strasbourg
P^ole API
Bd S´ebastien Brant, BP 10413
67412 Illkirch, CEDEX FRANCE
EMail : seo@lsiit.u-strasbg.fr
Web : http://lsiit.u-strasbg.fr
Tel : +33(0)3.68.85.45.58
Fax : +33(0)3.68.85.44.55
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PhD Student Position on: Shape Analysis and Registration of People
using Dynamic Data
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Location: LSIIT, University of Strasbourg, France (http://lsiit.u-strasbg.fr/)
PhD advisors: Hyewon SEO
https://lsiit.u-strasbg.fr/igg-fr/index.php/Hyewon_Seo
Dominique BECHMANN
http://lsiit.u-strasbg.fr/igg-en/index.php/Dominique_Bechmann
Starting date: September 2011
Ending date: 3 years from the starting date
Funding: This PhD topic is part of SHARED research project funded by
the French National Agency for Research (ANR). The net salary will be
approximately 1500 Euros per month (comfortable for living in France).
Context:
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Shape registration and analysis of people's surface dataset has become
a new mainstream, gradually replacing conventional methods based on
2-dimensional images. Across a variety of disciplines ranging from
anthropometry, computer aided design (CAD) computer graphics, and
psychology, adopting 3D laser scanners for surface shape capture and
building statistical models from a set of registered surface data is
now widely accepted. While there is a large amount of research done on
the static datasets with a proliferation of algorithms and a solid
theoretical background, this does not seem to be the case for dynamic,
time-varying datasets, due to the limited accessibility to the dynamic
surface. In most of the shape capture sessions, the person is required
to remain motionless during the scanning time. Naturally, current
registration techniques (and therefore shape analysis techniques)
handle the geometric features of static dataset, and the dynamic
behavior of people's skin relatively remain unsaid. This is
unfortunate, since dynamic features cannot be captured solely by using
geometric features when the target subjects undergo deformation.
Although the use of geometric feature based on anatomical knowledge is
still a golden standard, it is quite obvious that it may generate
results with limited capability of reliable correspondence
computation, because some commonly observed subjects like human body
are highly mobile and drastically change not only its spatial
arrangement but also geometric features over time.
Taking a step beyond the existing methods that use static shape
information for shape analysis, project SHARED seeks to investigate
novel shape analysis method that exploits large redundancy of
information from dynamic or movement data. The main interest of the
proposed approach is (1) to acquire and preprocess the subjects’
movement data so as to characterize anatomical or functional
landmarks, and (2) to devise a registration technique that makes use
of this rich set of information to guarantee reliable correspondence.
Appreciably, with the recent advances in imaging technologies we now
have growing accessibility to capture the shape and motion of human
skin from optical motion capture systems, and of organs from medical
imaging devices. (3) Further, we will investigate statistical analysis
of deforming shapes, tightly coupling the shape identity and shape
change due to movement. A statistical atlas spanning over the
variations of shape identity and shape deformation will be
constructed, which will be used to (4) revise the registration module
with great stability and robustness.
Proposed PhD topic:
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The first part of the thesis work deals with the choosing and
acquiring the dynamic data of the objects to be studied. We plan to
employ marker based methods in order to accurately locate and track
material points and minimize potential inaccuracies. We will then
develop data analysis methods, with a specific focus on the extraction
of dynamic features. Methods like strain analysis will be employed,
assuming dense, approximately regular spatial sampling of the
recovered 3D model is available at each time phase. Dynamic features
such as principle directions and magnitude of the deformation will be
identified on the surface, based on the analysis results.
In the second part, a reliable registration method will be developed,
based on the above mentioned dynamic features extracted. The idea is
to find the assignment between the source and the target so that the
consistency (or similarity) is maximized among the dynamic features.
This task will specifically require developing similarity measures
between vector fields, and/or tensors, and segmentation of the dynamic
shape data.
To apply:
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Please send your CV, a one-page letter of motivation and academic
transcriptions for the last 3 years to Dr. Hyewon SEO (seo@unistra.fr).
Qualification:
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- Master in Computer Science/Electrical Engineering, or Mathematics
- Good programming/communication skills
- Good level of English is an obligation.
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