Abstract-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.
Motten et al. REF trained a hierarchical classifier for selecting the most promising disparity with the matching costs and spatial relationship of pixels.
Trinocular disparity processor using a hierarchic classification structure