Smoothness preserving layout for dynamic labels by hybrid optimization.

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Computational Visual Media Pub Date : 2022-01-01 Epub Date: 2021-10-27 DOI:10.1007/s41095-021-0231-y
Yu He, Guo-Dong Zhao, Song-Hai Zhang
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引用次数: 0

Abstract

Stable label movement and smooth label trajectory are critical for effective information understanding. Sudden label changes cannot be avoided by whatever forced directed methods due to the unreliability of resultant force or global optimization methods due to the complex trade-off on the different aspects. To solve this problem, we proposed a hybrid optimization method by taking advantages of the merits of both approaches. We first detect the spatial-temporal intersection regions from whole trajectories of the features, and initialize the layout by optimization in decreasing order by the number of the involved features. The label movements between the spatial-temporal intersection regions are determined by force directed methods. To cope with some features with high speed relative to neighbors, we introduced a force from future, called temporal force, so that the labels of related features can elude ahead of time and retain smooth movements. We also proposed a strategy by optimizing the label layout to predict the trajectories of features so that such global optimization method can be applied to streaming data.

Electronic supplementary material: Supplementary material is available in the online version of this article at 10.1007/s41095-021-0231-y.

Abstract Image

基于混合优化的动态标签保持平滑布局。
稳定的标签运动和平滑的标签轨迹对于有效的信息理解至关重要。由于合力的不可靠性,任何强制定向方法都无法避免标签的突然变化,而全局优化方法由于不同方面的复杂权衡而无法避免。为了解决这一问题,我们提出了一种综合两种方法优点的混合优化方法。首先从特征的整个轨迹中检测出时空交点区域,并根据特征个数从小到大进行优化初始化布局。在时空交点区域之间的标签移动由力导向方法确定。为了应对一些相对于相邻特征速度较快的特征,我们引入了一种来自未来的力,称为时间力,使相关特征的标签能够提前避开时间而保持平滑运动。我们还提出了一种通过优化标签布局来预测特征轨迹的策略,使这种全局优化方法可以应用于流数据。电子补充材料:本文的在线版本为10.1007/s41095-021-0231-y。
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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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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