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An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images | Journal of Ambient Intelligence and Humanized Computing Skip to main content
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An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images

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Abstract

Object Stereo Vision has conventionally been one of the deeply examined areas in computer vision. Stereo matching is employed in numerous modern applications, including robot navigation, augmented reality, and automotive applications. Even though it has a long research history, it is still challenging for the edges of textureless, discontinues, and occluded regions under radiometric variation. This research article proposes a modified histogram equalization, a novel feature extraction, a spatial gradient model, and matching cost, which is robust and stable to images taken in different radiometric variations. The proposed method reduced the average percentage of bad pixels to 3.35 and reduced the relative mean square error (RMSE) up to 30.08 on the Middlebury dataset for different illumination and exposure values. Quantitative and qualitative evaluation of the proposed method demonstrates significant improvement in increasing PSNR and decreasing bad pixel percentage against radiometric variation and state-of-the-art local stereo matching algorithms.

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Authors and Affiliations

  1. National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan

    Qazi Mazhar ul Haq, Chang Hong Lin & Shanq-Jang Ruan

  2. Head of the Laboratory of Distributed Systems (LSD), National University of Asuncion, San Lorenzo, Paraguay

    Derlis Gregor

Authors
  1. Qazi Mazhar ul Haq
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  2. Chang Hong Lin
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  3. Shanq-Jang Ruan
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  4. Derlis Gregor
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Correspondence to Shanq-Jang Ruan.

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Haq, Q.M.u., Lin, C.H., Ruan, SJ. et al. An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images. J Ambient Intell Human Comput 13, 1953–1967 (2022). https://doi.org/10.1007/s12652-021-02958-8

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