<title>Segmentation and cooperative fusion of laser radar image data</title>

摘要

In segmentation, the goal is to partition a given 2D image into regions corresponding to the meaningful surfaces in the underlying physical scene. Segmentation is frequently a crucial step in analyzing and interpreting image data acquired by a variety of automated systems ranging from indoor robots to orbital satellites. In this paper, we present results of a study of segmentation by means of cooperative fusion of registered range and intensity images acquired using a prototype amplitude-modulated CW laser radar. In our approach, we consider three modalities--depth, reflectance and surface orientation. These modalities are modeled as sets of coupled Markov random fields for pixel and line processes. Bayesian inferencing is used to impose constraints of smoothness on the pixel process and linearity on the line process. The latter constraint is modeled using an Ising Hamiltonian. We solve the constrained optimization problem using a form of simulated annealing termed quenched annealing. The resulting model is illustrated in this paper in the rapid quenched, or iterated conditional mode, limit for several laboratory scenes.