.png){kind=icon}
返回
Contour tracking based on marginalized likelihood ratios
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2005.07.024.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
When fitting contour models to image data, it is necessary to take into account unmodelled shape variability. Traditionally, this has been done either by blurring the input image or by looking for image features in the neighborhood of the contour. A more statistically rigorous approach is to marginalize over all possible shape deformations. When this is done, the resulting likelihood model has similarities to both the blurring approach and the feature-based approach. A tracking application is used to demonstrate the marginalized likelihood model and compare it to the blurring approach. The best tracking results were obtained with the new model when combined with the Expeclation-Maxinlization (EM) algorithm. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
generative model
active contour
particle filters
EM algorithm
Kalman filter
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:
4.2
论文数:
4.1K
被引数:
6.7K
机构
暂无机构信息
引用论文
Composition, structure, and distribution of woody vegetation on the Cache River Floodplain, Arkansas.png)
Wetlands
WetlandsIF0
PREAI
分享收藏分享收藏
查看更多内容