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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiebo Luo Gray, R.T. Hsien-Che Lee |
| Copyright Year | 1998 |
| Description | Author affiliation: Imaging Res. & Adv. Dev., Eastman Kodak Co., Rochester, NY, USA (Jiebo Luo) |
| Abstract | In this study, we attempt to incorporate derivative measures into the scheme of adaptive Bayesian color image segmentation, and provide a unified perspective of region-based and edge-based statistical forces in segmentation. The standard Gibbs random field (GRF) is extended from being determined only by intensity (color) values, i.e., the zero-order derivatives, to also incorporating the first- and second-order derivatives. In particular, a significant first-order derivative accounts for discontinuities to help (1) obtain precise definition of region boundaries, and (2) overcome undesirable under-segmentation caused by the over-smoothing effect often introduced by a standard GRF; an insignificant second-order derivative necessitates stronger homogeneity constraints to help (1) further eliminate small segments caused by noise variations in statistically coherent regions, and (2) overcome undesirable over-segmentation of statistically ramp-like regions. Through balancing the three statistical forces derived from the zero-, first- and second-order derivatives, one can achieve physically and psychophysically more coherent image segmentation. |
| Starting Page | 780 |
| Ending Page | 784 |
| File Size | 1043701 |
| Page Count | 5 |
| File Format | |
| ISBN | 0818688211 |
| DOI | 10.1109/ICIP.1998.727372 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-10-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Color Image segmentation Force measurement Psychology |
| Content Type | Text |
| Resource Type | Article |
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