Math202 - Template

Digital Curvature Evolution Model for Image Segmentation

Recent works have indicated the potential of using curvature as a regularizer in image segmentation, in particular for the class of thin and elongated objects. These are ubiquitous in biomedical imaging (e.g. vascular networks), in which length regularization can sometime perform badly, as well as in texture identification. However, curvature is a second-order differential measure, and so its estimators are sensitive to noise. State-of-art techniques make use of a coarse approximation of curvature that limits practical applications. We argue that curvature must instead be computed using a multigrid convergent estimator, and we propose in this paper a new digital curvature flow which mimics continuous curvature flow. We illustrate its potential as a post-processing step to a variational segmentation framework.

Digital Curvature Flow

A preliminary version of the slides of DGCI. It was presented at LAMA during the seminar of the LIMD research group.

Math202 - TP4

Cryptanalyse du code de Vigenère

Math202 - TP3

Compression avec perte

Math202 - TP2

Compression de données avec l'algorithme LZ78

Curvature Regularization with Multigrid Convergent Estimators

Some previous attempts on models using multigrid convergent estimators. Presentation for the CoMeDiC research group.

Digital Curvature Flow (Flash)

5 minutes flash presentation of the motivation and results of the digital curvature flow presented during the winter school: Mathematics of Imaging at CIRM in Marseille.

MATH202 - TP1

Traitement d'images avec Python