Université d'Auvergne Clermont1 | CNRS



3 years 33 weeks ago
10/10/2013 - 15:30
Damien Gonzalez
ISIT, bâtiment 3C, faculté de médecine

Digital segmentation algorithms such as active contour models often use
signal parameters as energy.

Estimation of differentials is almost mandatory for most of them as they
use regularization terms like the snake algorithm.

The presentation will describe the fast level-wise convolution (LWC) and
its complexity of O(2n.log2(m)).

Finally i will show two LWC compatible kernel families.

3 years 36 weeks ago

Coste J, Derost P, Mulliez A, Gabrillargues J, Pereira B, Lemaire JJ (Dec 8-11, 2013) Contribution of local field potential to subthalamic nucleus deep brain stimulation in Parkinson’s disease (Talk) - 20th World Congress on Parkinson's Disease and Related Disorders (Geneva, Switzerland)

3 years 37 weeks ago

Coll G, Di-Rocco F, Brunelle F, Collet C, Arnaud E (September 10-14, 2013) Hydrocephalus and base skull abnormalities in FGRF 2 craniosynostosis (Poster) - International Society of Craniofacial Surgery, 15th Biennial Congress (Jackson Hole, Wyoming, USA)

3 years 37 weeks ago
10/09/2013 - 11:00
Vincent Nivoliers
Salle de réunion, ISIT, bâtiment 3C

This presentation will describe the optimisation of Restricted Voronoï Diagrams.
Such a structure is meant to partition a mesh between a set of sample points,
associating each point of the mesh to its nearest sample. With this tool, many
applications can be derived in sampling optimisation and mesh generation. I will
first describe how to optimize objective functions defined on Restricted Voronoï
Diagrams, and especially how to compute their gradient. I will then show how to
use these results to optimise a sampling of a general function defined on a
mesh. Finally, I will show how a distance between two meshes can be viewed as
an objective function on a Restricted Voronoï Diagram, and optimized to obtain a
surface fitting algorithm.

More material on the results I will be presenting can be found on my personal
web page : http://alice.loria.fr/~nivoliev in my PhD (French), and two of my
publications (Sampling Functions on a Mesh [...] and Fitting Polynomial Surfaces

3 years 48 weeks ago
25/06/2013 - 10:00
Pablo Mesejo
Bâtiment 3C, Faculté de médecine

This talk will be focused on the development of algorithms for the automatic segmentation of anatomical structures in biomedical images, particularly the hippocampus in histological images from the mouse brain. Such algorithms will be based on computer vision techniques and artificial intelligence methods. More precisely, on the one hand, we take advantage of statistical shape models to segment the anatomical structure under consideration and to embed the segmentation into an optimization framework. On the other hand, metaheuristics and classifiers are used to perform the optimization of the target function defined by the shape model (as well as to automatically tune the system parameters), and to refine the results obtained by the segmentation process, respectively. Different methods, with their corresponding advantages and disadvantages, will be introduced during the presentation.