Shape L'Âne rouge: Sliding wavelets for indexing and retrieval

Shape representation and retrieval of stored shape models are becoming increasingly more prominent in fields such as medical imaging, molecular biology and remote sensing. We present a novel framework that directly addresses the necessity for a rich and compressible shape representation, while simul...

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Veröffentlicht in:2008 IEEE Conference on Computer Vision and Pattern Recognition Jg. 2008; H. 4587838; S. 1 - 8
Hauptverfasser: Peter, Adrian, Rangarajan, Anand, Ho, Jeffrey
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2008
Schlagworte:
Biological system modeling
Biomedical imaging
Computational biology
Image retrieval
Indexing
Information geometry
Probability distribution
Remote sensing
Shape measurement
Wavelet coefficients
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
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Zusammenfassung:Shape representation and retrieval of stored shape models are becoming increasingly more prominent in fields such as medical imaging, molecular biology and remote sensing. We present a novel framework that directly addresses the necessity for a rich and compressible shape representation, while simultaneously providing an accurate method to index stored shapes. The core idea is to represent point-set shapes as the square root of probability densities expanded in a wavelet basis. We then use this representation to develop a natural similarity metric that respects the geometry of these probability distributions, i.e. under the wavelet expansion, densities are points on a unit hypersphere and the distance between densities is given by the separating arc length. The process uses a linear assignment solver for non-rigid alignment between densities prior to matching; this has the connotation of ldquoslidingrdquo wavelet coefficients akin to the sliding block puzzle LpsilaAne Rouge. We illustrate the utility of this framework by matching shapes from the MPEG-7 data set and provide comparisons to other similarity measures, such as Euclidean distance shape distributions.
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ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587838