• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.36-42
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• 2016

Recently, many 3D contents have been produced through the multiview camera system. In this system, since a difference of the viewpoint between color and depth cameras is inevitable, the camera parameter plays the important role to adjust the viewpoint as a preprocessing step. The conventional camera calibration method is inconvenient to users since we need to choose pattern features manually after capturing a planar chessboard with various poses. Therefore, we propose a semi-auto camera calibration method using a circular sampling and an homography estimation. Firstly, The proposed method extracts the candidates of the pattern features from the images by FAST corner detector. Next, we reduce the amount of the candidates by the circular sampling and obtain the complete point cloud by the homography estimation. Lastly, we compute the accurate position having the sub-pixel accuracy of the pattern features by the approximation of the hyper parabola surface. We investigated which factor affects the result of the pattern feature detection at each step. Compared to the conventional method, we found the proposed method released the inconvenience of the manual operation but maintained the accuracy of the camera parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.651-660
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• 2010

In this paper, we implement an efficient histogram-based prefiltering to compensate the illumination mismatches in regions between neighboring views. In multi-view video, such illumination disharmony can primarily occur on account of different camera location and orientation and an imperfect camera calibration. This discrepancy can cause the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be exploited to make up for these differences in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However general frames of multi-view video sequence are composed of several regions with different color composition and their histogram distribution which are mutually independent of each other. In addition, the location and depth of these objects from sequeuces captured from different cameras can be different with different frames. Thus we propose a new algorithm which classify a image into several subpartitions by its depth information first and then histogram matching is performed for each region individually. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional image-based algorithms.