.png){kind=icon}
返回
Manifold topological multi-resolution analysis method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2010.12.023.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 paper, two significant weaknesses of locally linear embedding (LLE) applied to computer vision are addressed: intrinsic dimension and eigenvector meanings. Topological embedding and multi-resolution nonlinearity capture are introduced based on mathematical analysis of topological manifolds and LLE. The manifold topological analysis (MTA) method is described and is based on topological embedding. MTA is a more robust method to determine the intrinsic dimension of a manifold with typical topology, which is important for tracking and perception understanding. The manifold multi-resolution analysis (MMA) method is based on multi-resolution nonlinearity capture. MMA defines LLE eigenvectors as features for pattern recognition and dimension reduction. Both MTA and MMA are proved mathematically, and several examples are provided. Applications in 3D object recognition and 3D object viewpoint space partitioning are also described. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Manifold topological analysis
Manifold multi-resolution analysis
Locally linear embedding
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。