NDLI: Learning a geometry integrated image appearance manifold from a small training set

Content Provider	IEEE Xplore Digital Library
Author	Yilei Xu Roy-Chowdhury, A.K.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Univ. of California, Riverside, CA (Yilei Xu; Roy-Chowdhury, A.K.)
Abstract	While low-dimensional image representations have been very popular in computer vision, they suffer from two limitations: (i) they require collecting a large and varied training set to learn a low-dimensional set of basis functions, and (ii) they do not retain information about the 3D geometry of the object being imaged. In this paper, we show that it is possible to estimate low-dimensional manifolds that describe object appearance while retaining the geometrical information about the 3D structure of the object. By using a combination of analytically derived geometrical models and statistical learning methods, this can be achieved using a much smaller training set than most of the existing approaches. Specifically, we derive a quadrilinear manifold of object appearance that can represent the effects of illumination, pose, identity and deformation, and the basis functions of the tangent space to this manifold depend on the 3D surface normals of the objects. We show experimental results on constructing this manifold and how to efficiently track on it using an inverse compositional algorithm.
Starting Page	1
Ending Page	8
File Size	429909
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational geometry Information geometry Computer vision Solid modeling Statistical learning Lighting Active appearance model Application software Data analysis Manifolds
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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