.png){kind=icon}
返回
A statistical approach to sparse multi-scale phase-based stereo
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2006.10.019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic model, where phase uncertainty is trained using data from a single image pair. The model is used to provide stable matches. The disparity algorithm and the probabilistic phase uncertainty model are verified on various stereo image pairs. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
stereo
probabilistic stereo
multi-scale
phase
steerable filter
orientation
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
The National Surgical Quality Improvement Program: learning from the past and moving to the future
PREAI
分享收藏
没有更多内容
没有更多内容