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Robust Depth Estimation and Image Fusion Based on Optimal Area Selection
AbstractMostly, 3D cameras having depth sensing capabilities employ active depth estimation techniques, such as stereo, the triangulation method or time-of-flight. However, these methods are expensive. The cost can be reduced by applying optical passive methods, as they are inexpensive and efficient. In this paper, we suggest the use of one of the passive optical methods named shape from focus (SFF) for 3D cameras. In the proposed scheme, first, an adaptive window is computed through an iterative process using a criterion. Then, the window is divided into four regions. In the next step, the best focused area among the four regions is selected based on variation in the data. The effectiveness of the proposed scheme is validated using image sequences of synthetic and real objects. Comparative analysis based on statistical metrics correlation, mean square error (MSE), universal image quality index (UIQI) and structural similarity (SSIM) shows the effectiveness of the proposed scheme.
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Lee, I.-H.; Mahmood, M.T.; Choi, T.-S. Robust Depth Estimation and Image Fusion Based on Optimal Area Selection. Sensors 2013, 13, 11636-11652.View more citation formats
Lee I-H, Mahmood MT, Choi T-S. Robust Depth Estimation and Image Fusion Based on Optimal Area Selection. Sensors. 2013; 13(9):11636-11652.Chicago/Turabian Style
Lee, Ik-Hyun; Mahmood, Muhammad T.; Choi, Tae-Sun. 2013. "Robust Depth Estimation and Image Fusion Based on Optimal Area Selection." Sensors 13, no. 9: 11636-11652.
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