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We designed a VLSI binocular vision system that emulates the disparity computation in the primary visual cortex (V1).
Owing to the combination of spatial filtering by analog parallel circuits and pixel-wise computation by hard-wired digital circuits, the present system can execute the disparity computation in real time using compact hardware.
Furthermore, significant changes in texture allow matching for disparity computation in most cases and do not result in holes.
Stereo algorithms generally involve the following four steps: i) matching cost computation, (ii) cost aggregation, (iii) disparity computation or optimization, and (iv) disparity refinement [1].
Due to the complexity of the matching process, most real-time stereo applications rely on local algorithms in the disparity computation.
The main demanding part is the disparity computation, up to 86%% of the time being dedicated to this task (cf. Fig. 17).
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Dynamic programming is a powerful method for solving energy minimisation problems in computer vision, for example stereo disparity computations.
In this paper a new hardware-efficient real-time disparity map computation module is developed.
Previous work on disparity map computation is mainly limited to software based techniques on general-purpose architectures.
The algorithm is based on a disparity map computation and an anaglyph synthesis.
Compared with the other disparity map computation techniques such as optical flow, stereo matching, wavelets, etc., the proposed approach produces a better performance according to the commonly used metrics (structural similarity and quantity of bad pixels).
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