DocumentCode :
2401527
Title :
Differential Structure in non-Linear Image Embedding Functions
Author :
Pless, Robert
Author_Institution :
Washington University in St. Louis
fYear :
2004
fDate :
27-02 June 2004
Firstpage :
10
Lastpage :
10
Abstract :
Many natural image sets are samples of a low dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of basis images, then linear dimensionality reduction techniques such as PCA and ICA fail, and non-linear dimensionality reduction techniques are required to automatically determine the intrinsic structure of the image set. Recent techniques such as ISOMAP and LLE provide a mapping between the images and a low dimensional parameterization of the images. In this paper we consider how choosing different image distance metrics affects the low-dimensional parameterization. For image sets that arise from non-rigid and human motion analysis, and MRI applications, differential motions in some directions of the low-dimensional space correspond to common transformations in the image domain. Defining distance measures that are invariant to these transformations makes Isomap a powerful tool for automatic registration of large image or video data sets.
Keywords :
Application software; Computer science; Computer vision; Embedded computing; Humans; Independent component analysis; Magnetic resonance imaging; Motion analysis; Pixel; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshop, 2004. CVPRW '04. Conference on
Type :
conf
DOI :
10.1109/CVPR.2004.49
Filename :
1384799
Link To Document :
بازگشت