论文信息 - On Detecting Spatial Regularity in Noisy Images

On Detecting Spatial Regularity in Noisy Images

Abstract Detecting spatial regularity in images arises in computer vision, scene analysis, military applications, and other areas. In this paper we present an O (n 5 2 ) algorithm that reports all maximal equally-spaced collinear subsets. The algorithm is robust in that it can tolerate noise or imprecision that may be inherent in the measuring process, where the error threshold is a user-specified parameter. Our method also generalizes to higher dimensions.

[1] Mark B. Sandler,et al. A combinatorial Hough transform , 1990, Pattern Recognit. Lett..

[2] O DudaRichard,et al. Use of the Hough transformation to detect lines and curves in pictures , 1972 .

[3] Andrew B. Kahng,et al. Optimal algorithms regularity in images for extracting spatial , 2022 .

[4] Leonidas J. Guibas,et al. Constructing Strongly Convex Approximate Hulls with Inaccurate Primitives , 1990, SIGAL International Symposium on Algorithms.

[5] Thomas Risse,et al. Hough transform for line recognition: Complexity of evidence accumulation and cluster detection , 1989, Comput. Vis. Graph. Image Process..

[6] Nimrod Megiddo,et al. Linear Programming in Linear Time When the Dimension Is Fixed , 1984, JACM.

[7] Laurence Boxer,et al. Parallel algorithms for all maximal equally-spaced collinear sets and all maximal regular coplanar lattices , 1993, Pattern Recognit. Lett..

[8] Richard O. Duda,et al. Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.