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» Application of Level Set Methods in Computer Vision
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FGR
2006
IEEE
255views Biometrics» more  FGR 2006»
13 years 11 months ago
Incremental Kernel SVD for Face Recognition with Image Sets
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such...
Tat-Jun Chin, Konrad Schindler, David Suter
ICPR
2008
IEEE
14 years 8 months ago
Multiple kernel learning from sets of partially matching image features
Abstract: Kernel classifiers based on Support Vector Machines (SVM) have achieved state-ofthe-art results in several visual classification tasks, however, recent publications and d...
Guo ShengYang, Min Tan, Si-Yao Fu, Zeng-Guang Hou,...
CVPR
2007
IEEE
14 years 9 months ago
Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update st...
Björn Stenger, Josef Kittler, Roberto Cipolla...
ECCV
2008
Springer
14 years 9 months ago
Online Sparse Matrix Gaussian Process Regression and Vision Applications
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
Ananth Ranganathan, Ming-Hsuan Yang
BMVC
1998
13 years 9 months ago
Planar Curve Representation and Matching
In this paper, we discuss a method for representing and matching planar curves. The technique is based on using calculations from concentric circles to represent each curve by two...
Maher Al-Khaiyat, Farhad Kamangar