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ECCV
2004
Springer
14 years 6 months ago
Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis
Abstract. We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the ...
Jason Meltzer, Ming-Hsuan Yang, Rakesh Gupta, Stef...
ICIP
2005
IEEE
13 years 10 months ago
View independent face recognition based on kernel principal component analysis of local parts
This paper presents a view independent face recognition method based on kernel principal component analysis (KPCA) of local parts. View changes induce large variation in feature s...
Koji Hotta
FGR
2006
IEEE
163views Biometrics» more  FGR 2006»
13 years 11 months ago
Human Action Recognition Using Multi-View Image Sequences Features
Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image se...
Mohiuddin Ahmad, Seong-Whan Lee
BMVC
1998
13 years 6 months ago
Building Shape Models from Image Sequences using Piecewise Linear Approximation
A method of extracting, classifying and modelling non-rigid shapes from an image sequence is presented. Shapes are approximated by polygons where the number of sides is related to...
Derek R. Magee, Roger D. Boyle
NECO
1998
151views more  NECO 1998»
13 years 4 months ago
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
Bernhard Schölkopf, Alex J. Smola, Klaus-Robe...