用可变形超曲面寻找时空边界

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Mathematical Imaging and Vision Pub Date : 2024-04-09 DOI:10.1007/s10851-024-01185-y
Patrick M. Jensen, J. Andreas Bærentzen, Anders B. Dahl, Vedrana A. Dahl
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引用次数: 0

摘要

动态三维成像技术越来越多地用于研究不断演变的物体。我们要解决的问题是检测和跟踪在时间上合并或分裂的简单物体。常见的解决方案包括检测拓扑变化。相反,我们利用物体只合并或只分裂时在 4D 中显示为单一成分这一观察结果,在 4D 中解决了这一问题。这样,我们就可以启动一个拓扑结构简单的三维超曲面,并对其进行变形,使其在任何时候都适合所有物体的表面。这样,物体的演变过程就得到了极其紧凑的呈现。我们在人工 4D 图像上测试了我们的方法,并将其与其他分割方法进行了比较。我们还将我们的方法应用于 4D X 射线数据集,以量化不断演变的拓扑结构。我们的方法与现有方法性能相当,资源利用率更高,鲁棒性更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Finding Space-Time Boundaries with Deformable Hypersurfaces

Dynamic 3D imaging is increasingly used to study evolving objects. We address the problem of detecting and tracking simple objects that merge or split in time. Common solutions involve detecting topological changes. Instead, we solve the problem in 4D by exploiting the observation that if objects only merge or only split, they appear as a single component in 4D. This allows us to initiate a topologically simple 3D hypersurface and deform it to fit the surface of all objects at all times. This gives an extremely compact representation of the objects’ evolution. We test our method on artificial 4D images and compare it to other segmentation methods. We also apply our method to a 4D X-ray data set to quantify evolving topology. Our method performs comparably to existing methods with better resource use and improved robustness.

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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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