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Part of book or chapter of book
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https://doi.org/10.1007/114080...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
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Data sources: DBLP
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Texture Mapping via Spherical Multi-dimensional Scaling

Authors: Asi Elad (Elbaz); Yosi Keller; Ron Kimmel;

Texture Mapping via Spherical Multi-dimensional Scaling

Abstract

We present a technique for texture mapping arbitrary sphere-like surfaces with minimal distortions by spherical embedding. The embedding is computed using spherical multi-dimensional scaling (MDS). MDS is a family of methods that map a set of points into a finite dimensional domain by minimizing the difference in distances between every pair of points in the original and the new embedding domains. In this paper spherical embedding is derived using geodesic distances on triangulated domains, computed by the fast marching method. The MDS is formulated as a non-linear optimization problem and a fast multi-resolution solution is derived. Finally, we show that the embedding of complex objects which are not sphere-like, can be improved by defining a texture dependent scale factor. This scale is the maximal distance to be preserved by the embedding and can be estimated using spherical harmonics. Experimental results show the benefits of the proposed approach.

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    19
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Average