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Depth estimation from image defocus using fuzzy logic

机译:使用模糊逻辑从图像散焦进行深度估计

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摘要

A method for improving the accuracy of depth-from-defocus is presented. Fuzzy logic is combined with a depth-from-defocus technique to correct for uncertainty and imprecision in depth estimation. Two inputs to the fuzzy algorithm are the focus quality and the focal error. Focus quality is a measure of the amount of defocus in an image. Focal error is the difference in focus between corresponding points in images with different apertures. The output is the depth estimation for objects in images that may be either blurred or in focus. Experiments show that fuzzy logic significantly improves depth estimation compared to the nonfuzzy depth-from-defocus method. The estimation error using fuzzy logic is less than 1.5% over an object distance from 7 to 11 feet. Therefore, this method improves the accuracy of the depth-from-defocus method, while maintaining simplicity. This method was implemented using a standard camera lens and an ANDROX imaging board.
机译:提出了一种提高离焦深度精度的方法。模糊逻辑与离焦深度技术相结合,可校正深度估计中的不确定性和不精确性。模糊算法的两个输入是聚焦质量和聚焦误差。聚焦质量是图像散焦量的度量。焦距误差是具有不同光圈的图像中相应点之间的焦点差异。输出是图像中可能模糊或聚焦的对象的深度估计。实验表明,与非模糊深度离焦方法相比,模糊逻辑显着改善了深度估计。在从7到11英尺的物体距离上,使用模糊逻辑的估计误差小于1.5%。因此,该方法在保持简单性的同时提高了离焦深度方法的精度。此方法是使用标准相机镜头和ANDROX成像板实现的。

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