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Robust Depth Estimation and Image Fusion Based on Optimal Area Selection
School of Mechatronics, Gwangju Institute of Science and Technology (GIST), 123 Cheomdan gwagiro, Buk-Gu, Gwangju 500-712, Korea
School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolno, Byeogchunmyun, Cheonan, Chungnam 330-708, Korea
* Author to whom correspondence should be addressed.
Received: 14 June 2013; in revised form: 26 August 2013 / Accepted: 27 August 2013 / Published: 4 September 2013
Abstract: Mostly, 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.
Keywords: depth estimation; optimal area selection; 3D camera
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Cite This Article
MDPI and ACS Style
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.
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.
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.