.png){kind=icon}
返回
Self-Adaptive Evolution Toward New Parameter Free Image Registration Methods
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2012.2209890.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image registration (IR) is a challenging topic in both the computer vision and pattern recognition fields; its main aim is to find the optimal transformation to provide the best overlay or fitting between two or more images. Usually, the success of well-known algorithms, such as iterative closest point, highly depends on several assumptions, e.g., the user should provide an initial near-optimal pose of the images to be registered. In the last decade, a new family of registration algorithms based on evolutionary principles has been contributed in order to overcome the latter drawbacks. However, their performance highly depends on carefully tuning (usually by hand) the control parameters of the algorithm, which is an error-prone and a time-consuming task. In this paper, we propose a new self-adaptive evolution model to deal with IR problems. To our knowledge, this is the first time a self-adaptive approach has been used for tuning the control parameters of evolutionary algorithms tackling computer vision tasks. Specifically, we introduce a novel design of the proposed self-adaptive approach facing pair-wise range IR problem instances, which is a challenging real-world optimization problem. In addition, several classical approaches, as well as state-of-the-art evolutionary IR methods, have been considered for numerical comparison.
Keyword:
3-D modeling
evolutionary algorithms (EAs)
image registration (IR)
range images
self-tuning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
分享收藏Responses of plants to environmental stresses, vol. I. 2nd edition I. Chilling, freezing and high temperature stresses.png)
Endeavour
EndeavourIF0
PREAI
分享收藏
Performance evaluation of memetic approaches in 3D reconstruction of forensic objectsSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏
查看更多内容