$\Gamma$-Convergence of Discrete Functionals with Nonconvex Perturbation for Image Classification
Gilles Aubert, Laure Blanc‐Féraud, Riccardo March · SIAM Journal on Numerical Analysis · 2004
The purpose of this paper is to show the theoretical soundness of a variational method proposed in image processing for supervised classification. Based on works developed for phase transitions in fluid mechanics, the classification is obtained by minimizing a sequence of functionals. The method provides an image composed of homogeneous regions with regular boundaries, a region being defined as a set of pixels belonging to the same class. In this paper, we show the $\Gamma$-convergence of the sequence of functionals which differ from the ones proposed in fluid mechanics in the sense that the perturbation term is not quadratic but has a finite asymptote at infinity, corresponding to an edge-preserving regularization term in image processing.