Trinocular disparity processor using a hierarchic classification structure

This paper presents a real-time trinocular disparity processor. The core module performs a pairwise segmented window matching for both the center-right and center-left image pair as their scaled down image pairs. The resulting cost functions are combined which results into nine different curves. A hierarchical classifier is presented which selects the most promising disparity value using information provided by the calculated cost curves and the pixels spatial neighborhood using a two level classification architecture. The disparity processor has been evaluated with an indoor dataset and with a real-time implementation using an FPGA and three cameras. Special care has been taken to reduce the memory footprint so that the processor doesn't need external memory.