Region based foreground segmentation combining color and depth sensors via logarithmic opinion pool decision

In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects' contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.