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Efficient hierarchical matching algorithm for processing uncalibrated stereo vision images and its hardware architecture

Efficient hierarchical matching algorithm for processing uncalibrated stereo vision images and its hardware architecture

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In motion estimation, the sub-pixel matching technique involves the search of sub-sample positions as well as integer-sample positions between the image pairs, choosing the one that gives the best match. Based on this idea, this work proposes an estimation algorithm, which performs a 2-D correspondence search using a hierarchical search pattern. The intermediate results are refined by 3-D cellular automata (CA). The disparity value is then defined using the distance of the matching position. Therefore the proposed algorithm can process uncalibrated and non-rectified stereo image pairs, maintaining the computational load within reasonable levels. Additionally, a hardware architecture of the algorithm is deployed. Its performance has been evaluated on both synthetic and real self-captured image sets. Its attributes, make the proposed method suitable for autonomous outdoor robotic applications.

Inspec keywords: cellular automata; stereo image processing; motion estimation; robot vision

Other keywords: sub-pixel matching technique; hardware architecture; hierarchical matching algorithm; uncalibrated stereo vision images; autonomous outdoor robotic applications; motion estimation; hierarchical search pattern; estimation algorithm; 3-D cellular automata

Subjects: Automata theory; Robotics; Computer vision and image processing techniques; Optical, image and video signal processing; Image sensors

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