A Multi-View Nonlinear Active Shape Model Using Kernel PCA | Zendy

Sami Romdhani | Zendy; Shaogang Gong | Zendy; Αλεξάνδρα Ψαρρού | Zendy

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A Multi-View Nonlinear Active Shape Model Using Kernel PCA

Author(s) -

Sami Romdhani,

Shaogang Gong,

Αλεξάνδρα Ψαρρού

Publication year - 1999

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.13.48

Subject(s) - kernel (algebra) , nonlinear system , constraint (computer aided design) , heat kernel signature , artificial intelligence , computer science , kernel principal component analysis , support vector machine , object (grammar) , pattern recognition (psychology) , transformation (genetics) , kernel method , feature (linguistics) , computer vision , algorithm , mathematics , active shape model , geometry , physics , quantum mechanics , biochemistry , chemistry , linguistics , philosophy , combinatorics , segmentation , gene

Recovering the shape of any 3D object using multiple 2D views requires establishing correspondence between feature points at different views. However changes in viewpoint introduce self-occlusions, resulting nonlinear variations in the shape and inconsistent 2D features between views. Here we introduce a multi-view nonlinear shape model utilising 2D view-dependent constraint without explicit reference to 3D structures. For nonlinear model transformation, we adopt Kernel PCA based on Support Vector Machines.

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