A biologically inspired active stereo vision system using a bottom-up saliency map model

Abstract

We propose a new active stereo vision system using a humanlike vergence control method. The proposed system uses a bottom-up saliency map model with a human-like selective attention function in order to select an interesting region in each camera. This system compares the landmarks as to whether the selective region in each camera finds the same region. If the left and right cameras successfully find the same landmark, the implemented vision system focuses on that landmark. Using motor encoder information, we can automatically obtain depth data even when occlusion problem occurs. Experimental results show that the proposed convergence method is very effective in implementing an active stereo system and it can also can be applied to a visual surveillance system for discriminating between a real human face and a photograph.