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Local stereo matching with adaptive shape support window based cost aggregation.

Yafan Xu, Yan Zhao, Mengqi Ji

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    |October 17, 2014
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    Summary
    This summary is machine-generated.

    This study introduces a new local stereo-matching algorithm using an adaptive shape support window (ASSW) for improved cost aggregation. The novel method effectively reduces errors in depth estimation, particularly at discontinuities.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Cost aggregation is crucial for local stereo-matching algorithms.
    • Existing methods often struggle with accuracy at depth discontinuities.

    Purpose of the Study:

    • To propose a novel local stereo-matching algorithm using an adaptive shape support window (ASSW).
    • To enhance cost aggregation accuracy and reduce errors in depth estimation.

    Main Methods:

    • Computed initial cost volume using intensity difference and gradient similarity.
    • Applied ASSW-based cost aggregation with a guided filter for edge-preserving smoothing.
    • Utilized winner-takes-all optimization and symmetric post-processing for disparity calculation.

    Main Results:

    • The proposed algorithm demonstrated superior matching performance on the Middlebury stereo benchmark.
    • Effectively reduced incorrect depth pixels, especially in regions with depth discontinuities and piecewise smooth areas.
    • Achieved better edge-preserving smoothing compared to bilateral filtering.

    Conclusions:

    • The ASSW-based cost aggregation method significantly improves local stereo-matching accuracy.
    • The algorithm is robust in challenging regions like depth discontinuities.
    • Offers a promising advancement in stereo vision research.