Name
Abstract | ||
|---|---|---|
We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape, and combined models, respectively. Indeed, snakes, modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/34.865192 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
Bayes methods,computer vision,covariance matrices,edge detection,principal component analysis,statistical analysis,Bayes method,Fourier descriptors,boundary detection,covariance matrices,principal component analysis,prior shape models,smoothness models,statistical variation modes | Point distribution model,Data set,Pattern recognition,Matrix (mathematics),Edge detection,Artificial intelligence,Covariance matrix,Smoothness,Principal component analysis,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
22 | 7 | 0162-8828 |
Citations | PageRank | References |
44 | 3.86 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongmei Wang | 1 | 232 | 23.10 |
| Lawrence H Staib | 2 | 1129 | 159.56 |