Janick Cardinale

TL;DR: Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

TL;DR: This work presents an alternating minimization algorithm to solve the resulting composite photometric/geometric inverse problem and derive the shape gradient of the data-fitting energy and investigate convex relaxation for the geometric problem.

TL;DR: An efficient discrete algorithm that allows minimizing a range of well-known energy functionals under the topological constraint of regions being connected components is presented.

TL;DR: This work proposes an alternating split Bregman algorithm for a large class of convex relaxations of the continuous Potts segmentation model and compares the algorithm to the primal-dual approach.

TL;DR: It is shown that the evaluation of Sobolev gradients amounts to particle-particle interactions along the contour in an image, and an existing particle-based segmentation algorithm is extended to using SoboleV gradients, which is 2.8 to 10 times faster than the previous reference implementation, but retains the known favorable properties of Sobolescu gradients.