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International Journal of Computer Vision

Erschienen in:

01.06.2015

Shape Description and Matching Using Integral Invariants on Eccentricity Transformed Images

verfasst von: Faraz Janan, Michael Brady

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2015

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Abstract

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with integral invariants (II) and with geodesic distance, yield signatures that are invariant to isometric deformations, such as bending and articulations. Integral invariants describe the boundaries of planar shapes. However, they provide no information about where a particular feature lies on the boundary with regard to the overall shape structure. Conversely, eccentricity transforms (Ecc) can match shapes by signatures of geodesic distance histograms based on information from inside the shape; but they ignore the boundary information. We describe a method that combines the boundary signature of a shape obtained from II and structural information from the Ecc to yield results that improve on them separately.

Vorheriger Artikel An Incremental Procedure for the Lateral Calibration of a Time-of-Flight Camera by One Image of a Flat Surface

Nächster Artikel Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization

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Titel: Shape Description and Matching Using Integral Invariants on Eccentricity Transformed Images
verfasst von: Faraz Janan
Michael Brady
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2015
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-014-0773-x

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An Incremental Procedure for the Lateral Calibration of a Time-of-Flight Camera by One Image of a Flat Surface

SPHORB: A Fast and Robust Binary Feature on the Sphere

Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization