Segmentation of objects by minimizing global-local variational energy
US7706610B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 29, 2005 |
| Grant date | Apr 27, 2010 |
| Priority date | — |
| Expiry date | Sep 3, 2028 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20116
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An “Image Segmenter” provides a variational energy formulation for segmentation of natural objects from images. In general, the Image Segmenter operates by adopting Gaussian mixture models (GMM) to capture the appearance variation of objects in one or more images. A global image data likelihood potential is then computed and combined with local region potentials to obtain a robust and accurate estimation of pixel foreground and background distributions. Iterative minimization of a “global-local energy function” is then accomplished by evolution of a foreground/background boundary curve by level set, and estimation of a foreground/background model by fixed-point iteration, termed “quasi-semi-supervised EM.” In various embodiments, this process is further improved by providing general object shape information for use in rectifying objects segmented from the image.
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