A spatial variant approach for vergence control in complex scenes

The flexibility in primate vision that utilizes active binocular vision is unparalleled even with modern fixed-stereo-based vision systems. However to follow the path of active binocular vision, the difficulty of attaining fixative capabilities is of primary concern. This paper presents a binocular vergence model that utilizes the retino-cortical log polar mapping in the primate vision system. Individual images of the binocular pair were converted to multi-resolution pyramids bearing a coarse-to-fine architecture (low resolution to high resolution) and disparity estimation on these pyramidal resolutions was performed using normalized cross correlation on the log polar images. The model was deployed on an actual binocular vergence system with independent pan-tilt controls for each camera and the system was able to robustly verge on objects even in cluttered environment with real time performance. This paper even presents the experimental results of the system functioning in unbalanced contrast exposures between the two cameras. The results proved favorable for real world robotic vision applications where noise is prevalent. The proposed vergence control model was also compared with a standard window based Cartesian stereo matching method and showed superior performance.