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基于最优区域选择的鲁棒深度估计和图像融合。

Robust depth estimation and image fusion based on optimal area selection.

机构信息

School of Mechatronics, Gwangju Institute of Science and Technology (GIST), Buk-Gu, Gwangju 500-712, Korea.

出版信息

Sensors (Basel). 2013 Sep 4;13(9):11636-52. doi: 10.3390/s130911636.

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.

摘要

大多数具有深度感应功能的 3D 相机采用主动深度估计技术,如立体视觉、三角测量法或飞行时间法。然而,这些方法成本高昂。通过应用光学被动方法可以降低成本,因为它们既便宜又高效。在本文中,我们建议将一种名为聚焦形状(SFF)的被动光学方法应用于 3D 相机。在提出的方案中,首先通过使用准则的迭代过程计算自适应窗口。然后,将窗口分为四个区域。下一步,根据数据的变化从四个区域中选择最佳聚焦区域。通过对合成和真实物体的图像序列进行验证,证明了所提出方案的有效性。基于统计指标相关性、均方误差 (MSE)、通用图像质量指数 (UIQI) 和结构相似性 (SSIM) 的比较分析表明了该方案的有效性。

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