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    ARTICLE

    Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

    H. Shalma, P. Selvaraj*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1145-1161, 2023, DOI:10.32604/iasc.2023.036853

    Abstract The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images. A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map. 3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps. This work uses the consistency check method to find an accurate depth map for identifying occluded pixels. The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation. The improved depth map quality within a… More >

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