Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.287 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
Shape,Diseases,Vectors,Humans,Principal component analysis,Optimization methods,Bones,Statistical analysis,Biology computing,Biological processes | Computer vision,Vector space,Locality,Symmetry group,Computer science,Image processing,Linear subspace,Surface shape,Artificial intelligence,Principal component analysis,Shape analysis (digital geometry) | Journal |
Volume | Issue | ISSN |
31 | 8 | 0162-8828 |
Citations | PageRank | References |
6 | 0.50 | 14 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan A. Alcantara | 1 | 144 | 7.76 |
| Owen T. Carmichael | 2 | 774 | 96.67 |
| Will Harcourt-smith | 3 | 18 | 1.78 |
| Kirstin Sterner | 4 | 6 | 0.50 |
| Stephen R. Frost | 5 | 6 | 0.50 |
| Rebecca Dutton | 6 | 352 | 24.16 |
| Paul Thompson | 7 | 127 | 13.46 |
| Eric Delson | 8 | 18 | 1.78 |
| Nina Amenta | 9 | 873 | 61.70 |