JetStream:粒子概率轮廓提取

P. Pérez, A. Blake, Michel Gangnet
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引用次数: 135

摘要

从噪声或杂乱图像中提取连续结构是一个难题。成功的提取关键取决于平衡先前对连续性和平滑性的限制与从图像分析中获得的证据的能力。精确的,确定性的优化算法,基于离散函数,遭受严重限制的形式,可以施加可跟踪的先验约束。本文提出了一种顺序蒙特卡罗技术,称为JetStream,它支持曲率、角和轮廓平行性的约束。在精确优化条件下,这些都是不可行的。JetStream的强大功能在两种情况下得到了展示:(1)照片编辑应用程序中的交互式切割,以及(2)航拍照片中的道路恢复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
JetStream: probabilistic contour extraction with particles
The problem of extracting continuous structures from noisy or cluttered images is a difficult one. Successful extraction depends critically on the ability to balance prior constraints on continuity and smoothness against evidence garnered from image analysis. Exact, deterministic optimisation algorithms, based on discretized functionals, suffer from severe limitations on the form of prior constraint that can be imposed tractably. This paper proposes a sequential Monte-Carlo technique, termed JetStream, that enables constraints on curvature, corners, and contour parallelism. To be mobilized, all of which are infeasible under exact optimization. The power of JetStream is demonstrated in two contexts: (1) interactive cut-out in photo-editing applications, and (2) the recovery of roads in aerial photographs.
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