不连续性分类
光度立体
间断(语言学)
解算器
离散化
边界(拓扑)
计算机科学
光学(聚焦)
各项异性扩散
计算机视觉
二次方程
分割
人工智能
正常
算法
数学
图像(数学)
几何学
数学优化
数学分析
曲面(拓扑)
光学
物理
作者
Yvain Quéau,Jean‐Denis Durou,Jean-François Aujol
标识
DOI:10.1007/s10851-017-0777-6
摘要
The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry. Inspired by edge-preserving methods from image processing, we study in this paper several variational approaches for normal integration, with a focus on non-rectangular domains, free boundary and depth discontinuities. We first introduce a new discretization for quadratic integration, which is designed to ensure both fast recovery and the ability to handle non-rectangular domains with a free boundary. Yet, with this solver, discontinuous surfaces can be handled only if the scene is first segmented into pieces without discontinuity. Hence, we then discuss several discontinuity-preserving strategies. Those inspired, respectively, by the Mumford–Shah segmentation method and by anisotropic diffusion, are shown to be the most effective for recovering discontinuities.
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