This scholarship is funded by the French National Agency for Research (ANR). The net salary will be approximately 1500 Euros per month (comfortable for living in France).
Course Starting date: September 2011
Deadline: 30 June 2011
The scholarship is for candidates interested in PhD on Shape Analysis and Registration of People using Dynamic Data
- Master in Computer Science/Electrical Engineering, or Mathematics
- Good programming/communication skills
- Good level of English is an obligation.
Please send your CV, a one-page letter of motivation and academic transcriptions for the last 3 years to Dr. Hyewon SEO (firstname.lastname@example.org).
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:
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.
Application Deadline : 30 June 2011
Contact Adress: email@example.com
Contact Email: firstname.lastname@example.org
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