进化图像转换与随机行走绘画

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aneta Neumann;Bradley Alexander;Frank Neumann
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引用次数: 7

摘要

我们提出了一项研究,展示了随机行走算法如何用于进化图像转换。我们基于均匀和有偏随机行走设计了不同的变异算子,并研究了它们与基线变异算子的组合如何在视觉效果和艺术特征方面产生有趣的图像转换过程。使用基于特征的分析,我们研究了不同特征的进化图像转换行为,并评估了在图像转换过程中构建的图像。之后,我们研究了如何将我们有偏差的随机行走方法的修改用于进化图像绘制。我们介绍了一种进化的图像绘画方法,其潜在的有偏差的随机行走可以通过影响随机行走的偏差的参数来控制,从而创造不同的艺术绘画效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolutionary Image Transition and Painting Using Random Walks
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process. Afterwards, we investigate how modifications of our biased random walk approaches can be used for evolutionary image painting. We introduce an evolutionary image painting approach whose underlying biased random walk can be controlled by a parameter influencing the bias of the random walk and thereby creating different artistic painting effects.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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