斜链代码的三维扩展:分析人类精子鞭毛搏动的曲折性

IF 3.7 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Andrés Bribiesca-Sánchez, Adolfo Guzmán, Fernando Montoya, Dan S. Díaz-Guerrero, Haydeé O. Hernández, Paul Hernández-Herrera, Alberto Darszon, Gabriel Corkidi, Ernesto Bribiesca
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引用次数: 0

摘要

在三维图像处理领域,准确表达线形曲线的几何细微差别至关重要。斜率链代码使用捕捉每个顶点外角的数组来巧妙地表示复杂的二维曲线,在此基础上,本研究引入了一种为多边形曲线量身定制的创新三维编码方法。这种三维编码方法采用平行斜率链和扭转链,确保了对平移、旋转和均匀缩放等常见变换的不变性,同时还证明了对镜像和可变起点的鲁棒性。该方法的一大特点是能够计算曲折度,这是曲线复杂性或缠绕性的描述符。通过将这一技术应用于生物医学工程,我们深入研究了人类精子的鞭毛跳动模式。这些洞察力强调了我们的三维编码在各种计算机视觉应用中的通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A three-dimensional extension of the slope chain code: analyzing the tortuosity of the flagellar beat of human sperm

A three-dimensional extension of the slope chain code: analyzing the tortuosity of the flagellar beat of human sperm

In the realm of 3D image processing, accurately representing the geometric nuances of line curves is crucial. Building upon the foundation set by the slope chain code, which adeptly represents intricate two-dimensional curves using an array capturing the exterior angles at each vertex, this study introduces an innovative 3D encoding method tailored for polygonal curves. This 3D encoding employs parallel slope and torsion chains, ensuring invariance to common transformations like translations, rotations, and uniform scaling, while also demonstrating robustness against mirror imaging and variable starting points. A hallmark feature of this method is its ability to compute tortuosity, a descriptor of curve complexity or winding nature. By applying this technique to biomedical engineering, we delved into the flagellar beat patterns of human sperm. These insights underscore the versatility of our 3D encoding across diverse computer vision applications.

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来源期刊
Pattern Analysis and Applications
Pattern Analysis and Applications 工程技术-计算机:人工智能
CiteScore
7.40
自引率
2.60%
发文量
76
审稿时长
13.5 months
期刊介绍: The journal publishes high quality articles in areas of fundamental research in intelligent pattern analysis and applications in computer science and engineering. It aims to provide a forum for original research which describes novel pattern analysis techniques and industrial applications of the current technology. In addition, the journal will also publish articles on pattern analysis applications in medical imaging. The journal solicits articles that detail new technology and methods for pattern recognition and analysis in applied domains including, but not limited to, computer vision and image processing, speech analysis, robotics, multimedia, document analysis, character recognition, knowledge engineering for pattern recognition, fractal analysis, and intelligent control. The journal publishes articles on the use of advanced pattern recognition and analysis methods including statistical techniques, neural networks, genetic algorithms, fuzzy pattern recognition, machine learning, and hardware implementations which are either relevant to the development of pattern analysis as a research area or detail novel pattern analysis applications. Papers proposing new classifier systems or their development, pattern analysis systems for real-time applications, fuzzy and temporal pattern recognition and uncertainty management in applied pattern recognition are particularly solicited.
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