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  • Open Access

    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 >

  • Open Access

    ARTICLE

    Convergence of Stereo Vision-Based Multimodal YOLOs for Faster Detection of Potholes

    Sungan Yoon, Jeongho Cho*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2821-2834, 2022, DOI:10.32604/cmc.2022.027840

    Abstract Road potholes can cause serious social issues, such as unexpected damages to vehicles and traffic accidents. For efficient road management, technologies that quickly find potholes are required, and thus researches on such technologies have been conducted actively. The three-dimensional (3D) reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost. The two-dimensional (2D) vision method has the advantage of inexpensive and easy application of sensor. Recently, although the 2D vision method using the convolutional neural network (CNN) has shown improved pothole… More >

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