二维规则网格中旋转的数字连续性

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Müge Saadetoğlu, Benedek Nagy, Aydın Avkan
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引用次数: 0

摘要

如果两个相邻像素在运动后仍保持相邻,则称为数字化连续刚性运动。当人们或计算机(人工智能、机器视觉)需要识别图像中显示的物体时,这一概念发挥着重要作用。在本文中,我们关注的是像素与其近邻的数字旋转。当旋转中心为主要像素的中点、网格点(像素的角落)或边缘中点时,我们比较了三种规则网格的邻域运动图结果。数字旋转质量的第一个衡量标准是基于双射性,例如,衡量有多少情况下产生双射邻域运动图,有多少情况下产生非双射邻域运动图(Avkan 等人,2022 年)。现在,我们研究了第二种测量方法,即通过结果图像的数字连续性来测量双射数字旋转的质量:我们测量有多少情况是双射的,同时也是数字连续的。我们发现,相对于正方形网格或六边形网格的三个不同旋转中心,三角形网格上的旋转被证明在更多的实角上是数字连续的,而且作为特例,在更多的整数角上也是数字连续的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital continuity of rotations in the 2D regular grids

A digitized rigid motion is called digitally continuous if two neighbor pixels still stay neighbors after the motion. This concept plays important role when people or computers (artificial intelligence, machine vision) need to recognize the object shown in the image. In this paper, digital rotations of a pixel with its closest neighbors are of our interest. We compare the neighborhood motion map results among the three regular grids, when the center of rotation is the midpoint of a main pixel, a grid point (corner of a pixel) or an edge midpoint. The first measure about the quality of digital rotations is based on bijectivity, e.g., measuring how many of the cases produce bijective and how many produce not bijective neighborhood motion maps (Avkan et. al, 2022). Now, a second measure is investigated, the quality of bijective digital rotations is measured by the digital continuity of the resulted image: we measure how many of the cases are bijective and also digitally continuous. We show that rotations on the triangular grid prove to be digitally continuous at many more real angles and also as a special case, many more integer angles compared to the square grid or to the hexagonal grid with respect to the three different rotation centers.

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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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