基于可见度锥计算矩形瓦片集的最小周长多边形

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Petra Wiederhold
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引用次数: 0

摘要

为了研究数字平面对象的凹凸特性,20 世纪 70 年代,Sklansky、Chazin、Hansen、Kibler 和 Kim 在文章中定义了最小周长多边形(MPP),将像素与马赛克中的多边形瓷砖进行识别,并提出了两种算法(1972 年和 1976 年)来确定 MPP 的顶点。这些算法都是基于能见度锥的构建和迭代限制,而 MPP 顶点则是瓦片的特殊顶点。本文提出了一种新的 MPP 算法,适用于矩形马赛克中作为规则复合物给出的对象,这些规则复合物是边缘相接的瓦片集,既没有末端瓦片,也没有孔洞,其边界不一定是简单的。新算法将典型边界路径作为输入,我们还提出了一种边界追踪算法来获取该路径。我们回顾了用于矩形瓷砖的两种经典 MPP 算法,以及广泛使用的现代数字图像分析教科书(2018 年,2020 年)中推荐的用于正方形瓷砖的简化改编算法,以生成简单数字 4 轮廓的近似值。我们证明了所有这些算法都是失败的,它们的数学基础存在缺陷,我们纠正了这些错误,开发了新的 MPP 算法。我们用实例说明了我们的 MPP 算法,并证明了其正确性。根据我们的假设,MPP 与多边形 \(B\supset A\) 的集合 A 的相对凸壳重合,其中 A 不一定是多边形,甚至不一定是连通的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computing the Minimal Perimeter Polygon for Sets of Rectangular Tiles based on Visibility Cones

Computing the Minimal Perimeter Polygon for Sets of Rectangular Tiles based on Visibility Cones

To study convexity properties of digital planar objects, the minimum perimeter polygon (MPP) was defined in the 1970 s in articles by Sklansky, Chazin, Hansen, Kibler, and Kim, where pixels were identified with polygonal tiles in mosaics, and two algorithms (1972, 1976) were proposed to determine the MPP vertices. These algorithms are based on constructing and iteratively restricting visibility cones, the MPP vertices result as special vertices of the tiles. The present paper proposes a novel MPP algorithm for objects given as regular complexes in rectangular mosaics, which are edge-adjacency-connected sets of tiles that have neither end tiles nor holes and whose boundaries not necessarily are simple. The new algorithm takes as input the canonical boundary path, we also propose a boundary tracing algorithm to obtain this path. We review the two classic MPP algorithms for rectangular tiles and a simplified adaptation for square tiles that is recommended in widely used modern textbooks on digital image analysis (2018, 2020) to produce approximations of simple digital 4-contours. We show that all these algorithms fail and that their mathematical basis is flawed, we correct the errors to develop the new MPP algorithm. Our MPP algorithm is illustrated using examples and its correctness is proved. Under our assumptions, the MPP coincides with the relative convex hull of a set A with respect to a polygon \(B\supset A\), where A is not necessarily a polygon, not even connected.

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来源期刊
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision 工程技术-计算机:人工智能
CiteScore
4.30
自引率
5.00%
发文量
70
审稿时长
3.3 months
期刊介绍: The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles. Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications. The scope of the journal includes: computational models of vision; imaging algebra and mathematical morphology mathematical methods in reconstruction, compactification, and coding filter theory probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science inverse optics wave theory. Specific application areas of interest include, but are not limited to: all aspects of image formation and representation medical, biological, industrial, geophysical, astronomical and military imaging image analysis and image understanding parallel and distributed computing computer vision architecture design.
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