Exploration of shape variation using localized components analysis

Dan A. Alcantara; Owen Carmichael; Will Harcourt-Smith; Kirstin Sterner; Stephen R. Frost; Rebecca Dutton; Paul Thompson; Eric Delson; Nina Amenta

doi:10.1109/TPAMI.2008.287

Exploration of shape variation using localized components analysis

Dan A. Alcantara, Owen Carmichael, Will Harcourt-Smith, Kirstin Sterner, Stephen R. Frost, Rebecca Dutton, Paul Thompson, Eric Delson, Nina Amenta

Neurology

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Localized Components Analysis (LoCA) is a new method for describing surface shape variation in an ensemble of objects using a linear subspace of spatially localized shape components. In contrast to earlier methods, LoCA optimizes explicitly for localized components and allows a flexible trade-off between localized and concise representations, and the formulation of locality is flexible enough to incorporate properties such as symmetry. This paper demonstrates that LoCA can provide intuitive presentations of shape differences associated with sex, disease state, and species in a broad range of biomedical specimens, including human brain regions and monkey crania.

Original language	English (US)
Pages (from-to)	1510-1516
Number of pages	7
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	31
Issue number	8
DOIs	https://doi.org/10.1109/TPAMI.2008.287
State	Published - 2009

Keywords

Feature representation
Life and medical sciences
Size and shape

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition
Software
Computational Theory and Mathematics
Applied Mathematics

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10.1109/TPAMI.2008.287

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Exploration of shape variation using localized components analysis. / Alcantara, Dan A.; Carmichael, Owen; Harcourt-Smith, Will; Sterner, Kirstin; Frost, Stephen R.; Dutton, Rebecca; Thompson, Paul; Delson, Eric; Amenta, Nina.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 8, 2009, p. 1510-1516.

Research output: Contribution to journal › Article › peer-review

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