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3D Shape from Focus Using LULU Operators

机译:使用LULU运算符从焦点获得3D形状

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摘要

Extracting the shape of an object is one of the important tasks to be performed in many vision applications. One of the difficult challenges in 3D shape extraction is the roughness of the surfaces of objects. Shape from focus (SFF) is a shape recovery method that reconstructs the shape of an object from a sequence of images taken from the same viewpoint but with different focal lengths. This paper proposes the use of LULU operators as a preprocessing step to improve the signal-to-noise ratio in the estimation of 3D shape from focus. LULU operators are morphological filters that are used for their structure preserving properties. The proposed technique is tested on simulated and real images separately, as well as in combination with traditional SFF methods such as sum modified Laplacian (SML), and gray level variance (GLV). The proposed technique is tested in the presence of impulse noise with different noise levels. Based on the quantitative and qualitative experimental results it is shown that the proposed techniques is more accurate in focus value extraction and shape recovery in the presence of noise.
机译:提取对象的形状是许多视觉应用程序中要执行的重要任务之一。 3D形状提取中的困难挑战之一是物体表面的粗糙度。聚焦形状(SFF)是一种形状恢复方法,可从从相同视点但具有不同焦距的一系列图像中重建对象的形状。本文提出使用LULU算子作为预处理步骤,以提高从焦点估计3D形状时的信噪比。 LULU运算符是用于保持其结构性质的形态过滤器。所提出的技术分别在模拟图像和真实图像上进行测试,并与传统的SFF方法(例如,总和修正的Laplacian(SML)和灰度级方差(GLV))相结合进行测试。在存在不同噪声水平的脉冲噪声的情况下对提出的技术进行了测试。基于定量和定性的实验结果,表明所提出的技术在存在噪声的情况下在聚焦值提取和形状恢复方面更准确。

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