Title of article :
Manifold topological multi-resolution analysis method
Author/Authors :
You، نويسنده , , Shaodi and Ma، نويسنده , , Huimin، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
20
From page :
1629
To page :
1648
Abstract :
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.
Keywords :
Manifold topological analysis , Manifold multi-resolution analysis , Locally linear embedding
Journal title :
PATTERN RECOGNITION
Serial Year :
2011
Journal title :
PATTERN RECOGNITION
Record number :
1734097
Link To Document :
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