FUSION OF RANGE AND INTENSITY IMAGES ON A CONNECTION MACHINE (CM-2)

A new algorithm for fusion of range and intensity images in a Bayesian framework is presented. This framework allows the fusion problem to be posed in terms of maximization of a posteriori density function or, equivalently, minimization of an energy function. Experimental results of the fusion algorithm on registered range and intensity images indicate that the detected and labeled edges match the physical edges in the scene. To alleviate the heavy computational demands of our fusion algorithm, two energy minimization algorithms have been implemented on a CM-2 Connection Machine, an SIMD type parallel hardware. It is demonstrated that a straightforward conversion of sequential code to parallel code results in poor computational performance. Experimental results show that computations in the fusion energy minimization operation can be carried out significantly faster (speedup > 50) on the CM-2 than on a Sun Sparcstation-2.