Segmentation based on fusion of range and intensity images using robust trimmed methods

摘要

This paper proposes a segmentation algorithm based on fusion of range and intensity images using robust trimmed methods. Based on the Bayesian theory, a priori knowledge is represented using the Markov random field (MRF). A maximum a posteriori (MAP) estimator is constructed using the edge features extracted from both range and intensity images. Objects are represented by a number of local planar surfaces in range images, and the parametric space for surface representation is constructed with the surface parameters estimated pixel-by-pixel based on the least trimmed squares (LTS) method. Whereas in intensity images, the α-trimmed variance is adopted as the feature for edge extraction. A final edge map is obtained by the MAP estimator that is constructed using the likelihood functions based on the edge information obtained from range and intensity images. Finally, an image is segmented using the fused edge map. Computer simulation results show that our new segmentation algorithm effectively segments test images, independent of shadow, noise, and lighting environment.