A dense stereo correspondence algorithm for hardware implementation with enhanced disparity selection

In this paper an effective, hardware oriented stereo correspondence algorithm, able to produce dense disparity maps of improved fidelity is presented. The proposed algorithm combines rapid execution, simple and straight-forward structure as well as comparably high quality of results. These features render it as an ideal candidate for hardware implementation and for real-time applications. The proposed algorithm utilizes the Absolute Differences (AD) as matching cost and aggregates the results inside support windows, assigning Gaussian distributed weights to the support pixels, based on their Euclidean distance. The resulting Disparity Space Image (DSI) is furthered refined by Cellular Automata (CA) acting in all of the three dimensions of the DSI. The algorithm is applied to typical as well as to self-recorded real-life image sets. The disparity maps obtained are presented and quantitatively examined