An approach toward fast gradient-based image segmentation
| Autor: | Benjamin Hell, Marc Kassubeck, Martin Eisemann, Pablo Bauszat, Marcus Magnor |
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| Rok vydání: | 2015 |
| Předmět: |
Optimization problem
business.industry Segmentation-based object categorization ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Image segmentation Computer Graphics and Computer-Aided Design Image texture Minimum spanning tree-based segmentation Region growing Computer Science::Computer Vision and Pattern Recognition Image Processing Computer-Assisted Animals Humans Computer vision Artificial intelligence Range segmentation business Software Algorithms Mathematics |
| Zdroj: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 24(9) |
| ISSN: | 1941-0042 |
| Popis: | In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford–Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex–concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images (Fig. 1). |
| Databáze: | OpenAIRE |
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