Color image segmentation using iterative edge cutting, NUV-EM, and Gaussian message passing
| Autor: | Hans-Andrea Loeliger, Robert Torfason, Boxiao Ma, Nour Zalmai, Carina Striti |
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| Rok vydání: | 2017 |
| Předmět: |
Markov random field
060102 archaeology Pixel Computer science Gaussian Message passing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 06 humanities and the arts 02 engineering and technology Image segmentation Grayscale symbols.namesake Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering Piecewise symbols 020201 artificial intelligence & image processing 0601 history and archaeology Algorithm |
| Zdroj: | GlobalSIP |
| DOI: | 10.1109/globalsip.2017.8308624 |
| Popis: | A new approach to image segmentation (grayscale or color) is proposed. It uses a (improper) Markov random field prior with sparsifying NUV terms (normal with unknown variance), which favors piecewise smooth images with sharp edges. The proposed algorithm iterates two steps. In the first step, the unknown scalar variances are learned by approximate EM (expectation maximization). The actual computations for this step boil down to iterative scalar Gaussian message passing, which scales linearly with the number of pixels. In the second step, all edges that were detected in the first step are cut and removed from further processing. Simulation results demonstrate that the proposed approach compares favorably with four standard methods. |
| Databáze: | OpenAIRE |
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