?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contribut...
— We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. Roughly speaking, a candidate optimizer is an approximate dom...
Andrea Lecchini-Visintini, John Lygeros, Jan M. Ma...
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
Simulated annealing (SA) and deterministic continuation are well-known generic approaches to global optimization. Deterministic continuation is computationally attractive but produ...
We introduce a new interpretation of multiscale random fields (MSRFs) that admits efficient optimization in the framework of regular (single level) random fields (RFs). It is base...
Longin Jan Latecki, ChengEn Lu, Marc Sobel, Xiang ...