Medical image segmentation, the task of partitioning an image into meaningful parts, is an
important step toward automating medical image analysis and is at the crux of a variety of …

A new algorithm is proposed to project, exactly and in finite time, a vector of arbitrary size
onto a simplex or an l_1 l 1-norm ball. It can be viewed as a Gauss–Seidel-like variant of …

These notes address various theoretical and practical topics related to Total Variationbased
image reconstruction. They focus first on some theoretical results on functions which …

This paper presents a new approach for hyperspectral image classification exploiting
spectral-spatial information. Under the maximum a posteriori framework, we propose a …

We propose and study novel max-flow models in the continuous setting, which directly map
the discrete graph-based max-flow problem to its continuous optimization formulation. We …

In this work we propose a convex relaxation approach for computing minimal partitions. Our
approach is based on rewriting the minimal partition problem (also known as Potts model) in …

We describe a convex relaxation for a family of problems of minimal perimeter partitions. The
minimization of the relaxed problem can be tackled numerically: we describe an algorithm …

We address the continuous problem of assigning multiple (unordered) labels with the
minimum perimeter. The corresponding discrete Potts model is typically addressed with a …

The active contours without edges model of Chan and Vese (IEEE Transactions on Image
Processing 10 (2): 266–277, 2001) is a popular method for computing the segmentation of …

In this paper, we introduce a mathematical framework for obtaining spatially smooth
semantic labelings of 3D point clouds from a pointwise classification. We argue that …