二维笛卡尔网格中的同伦仿射变换

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nicolas Passat, Phuc Ngo, Yukiko Kenmochi, Hugues Talbot
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引用次数: 1

摘要

拓扑保持是中仿射变换的一个性质 \({{{\mathbb {R}}}^2}\),但不是。 \({{\mathbb {Z}}}^2\). 在本文中,给定一个二进制对象 \({\mathsf {X}} \subset {{\mathbb {Z}}}^2\) 一个仿射变换 \({{\mathcal {A}}}\),我们提出了一种构建二进制对象的方法 \(\widehat{{\mathsf {X}}} \subset {{\mathbb {Z}}}^2\) 的应用而产生的 \({{\mathcal {A}}}\) on \({\mathsf {X}}\). 我们的目的是,特别地,保持之间的同伦类型 \({\mathsf {X}}\) 和 \(\widehat{{\mathsf {X}}}\). 为此,我们制定了建设 \(\widehat{{\mathsf {X}}}\) 从 \({{\mathsf {X}}}\) 作为一个在细胞复合体空间中的优化问题,我们在拓扑约束下求解这个问题。更准确地说,我们定义了一个细胞空间 \({{\mathbb {H}}}\) 通过两个细胞空间的叠加 \({{\mathbb {F}}}\) 和 \({{\mathbb {G}}}\) 的标准笛卡尔网格 \({{\mathbb {Z}}}^2\) 在哪里 \({{\mathsf {X}}}\) 定义,并由仿射变换诱导出一个规则网格 \({{{\mathcal {A}}}}\),分别。对象 \(\widehat{{\mathsf {X}}}\) 然后通过在空间内建立一个同伦变换来计算吗 \({{\mathbb {H}}}\),从复合体中出发 \({{\mathbb {G}}}\) 由…的转变而产生的 \({\mathsf {X}}\) 关于 \({{\mathcal {A}}}\) 最后是一个复杂的拟合 \(\widehat{{\mathsf {X}}}\) 在 \({{\mathbb {F}}}\) 可以嵌入到 \({{\mathbb {Z}}}^2\).
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Homotopic Affine Transformations in the 2D Cartesian Grid

Homotopic Affine Transformations in the 2D Cartesian Grid

Topology preservation is a property of affine transformations in \({{{\mathbb {R}}}^2}\), but not in \({{\mathbb {Z}}}^2\). In this article, given a binary object \({\mathsf {X}} \subset {{\mathbb {Z}}}^2\) and an affine transformation \({{\mathcal {A}}}\), we propose a method for building a binary object \(\widehat{{\mathsf {X}}} \subset {{\mathbb {Z}}}^2\) resulting from the application of \({{\mathcal {A}}}\) on \({\mathsf {X}}\). Our purpose is, in particular, to preserve the homotopy type between \({\mathsf {X}}\) and \(\widehat{{\mathsf {X}}}\). To this end, we formulate the construction of \(\widehat{{\mathsf {X}}}\) from \({{\mathsf {X}}}\) as an optimization problem in the space of cellular complexes, and we solve this problem under topological constraints. More precisely, we define a cellular space \({{\mathbb {H}}}\) by superimposition of two cellular spaces \({{\mathbb {F}}}\) and \({{\mathbb {G}}}\) corresponding to the canonical Cartesian grid of \({{\mathbb {Z}}}^2\) where \({{\mathsf {X}}}\) is defined, and a regular grid induced by the affine transformation \({{{\mathcal {A}}}}\), respectively. The object \(\widehat{{\mathsf {X}}}\) is then computed by building a homotopic transformation within the space \({{\mathbb {H}}}\), starting from the complex in \({{\mathbb {G}}}\) resulting from the transformation of \({\mathsf {X}}\) with respect to \({{\mathcal {A}}}\) and ending at a complex fitting \(\widehat{{\mathsf {X}}}\) in \({{\mathbb {F}}}\) that can be embedded back into \({{\mathbb {Z}}}^2\).

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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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