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  • 1
    Online Resource
    Online Resource
    Institute of Advanced Engineering and Science ; 2023
    In:  Bulletin of Electrical Engineering and Informatics Vol. 12, No. 1 ( 2023-02-01), p. 328-337
    In: Bulletin of Electrical Engineering and Informatics, Institute of Advanced Engineering and Science, Vol. 12, No. 1 ( 2023-02-01), p. 328-337
    Abstract: Stereo matching algorithm plays an important role in an autonomous vehicle navigation system to ensure accurate three-dimensional (3D) information is provided. The disparity map produced by the stereo matching algorithm directly impacts the quality of the 3D information provided to the navigation system. However, the accuracy of the matching algorithm is a challenging part to be solved since it is directly affected by the surrounding environment such as different brightness, less texture surface, and different image pair exposure. In this paper, a new framework of stereo matching algorithm that used the integration of census transform (CT) and sum of absolute difference (SAD) at the matching cost computation step, non-local cost aggregation at the second step, winner take all strategy at the third step, and a median filter at the final step to minimize disparity map error. The results show that the accuracy of the disparity map is improved using the proposed methods after some parameter adjustment. Based on the standard Middlebury and KITTI benchmarking dataset, it shows that the proposed framework produced accurate results compared with other established methods.
    Type of Medium: Online Resource
    ISSN: 2302-9285 , 2089-3191
    URL: Issue
    Language: Unknown
    Publisher: Institute of Advanced Engineering and Science
    Publication Date: 2023
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  • 2
    In: International Journal of Emerging Technology and Advanced Engineering, IJETAE Publication House, Vol. 11, No. 6 ( 2021-6-16), p. 86-96
    Abstract: Fundamentally, a stereo matching algorithm produces a disparity map or depth map. This map contains valuable information for many applications, such as range estimation, autonomous vehicle navigation and 3D surface reconstruction. The stereo matching process faces various challenges to get an accurate result for example low texture area, repetitive pattern and discontinuity regions. The proposed algorithm must be robust and viable with all of these challenges and is capable to deliver good accuracy. Hence, this article proposes a new stereo matching algorithm based on a hybrid Convolutional Neural Network (CNN) combined with directional intensity differences at the matching cost stage. The proposed algorithm contains a deep learning-based method and a handcrafted method. Then, the bilateral filter is used to aggregate the matching cost volume while preserving the object edges. The Winner-Take-All (WTA) is utilized at the optimization stage which the WTA normalizes the disparity values. At the last stage, a series of refinement processes will be applied to enhance the final disparity map. A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. This dataset provides images with the characteristics of low texture area, repetitive pattern and discontinuity regions. The average error produced for all pixel regions is 8.51%, while the nonoccluded region is 5.77%. Based on the experimental results, the proposed algorithm produces good accuracy and robustness against the stereo matching challenges. It is also competitive with other published methods and can be used as a complete algorithm
    Type of Medium: Online Resource
    ISSN: 2250-2459
    URL: Issue
    Language: Unknown
    Publisher: IJETAE Publication House
    Publication Date: 2021
    Location Call Number Limitation Availability
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