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3D Shape from Focus Using LULU Operators

机译:3D从焦点的形状使用lulu运算符

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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:提取对象的形状是在许多视觉应用中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。来自焦点(SFF)的形状是一种形状恢复方法,其从从同一视点拍摄的图像序列重建对象的形状,而是具有不同的焦距。本文提出使用Lulu运营商作为预处理步骤,以提高焦点3D形状估计中的信噪比。 Lulu运营商是用于它们的结构保持性质的形态过滤器。所提出的技术单独测试模拟和真实图像,以及与传统的SFF方法组合,例如SUM改性LAPLACIAN(SML)和灰度方差(GLV)。在具有不同噪声水平的脉冲噪声存在下测试所提出的技术。基于定量和定性实验结果,表明该技术在噪声存在下的聚焦值提取和形状恢复方面更准确。

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