Variational multi-phase segmentation using high-dimensional local features
| Autor: | Benjamin Berkels, Niklas Mevenkamp |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Feature vector Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Initialization Pattern recognition 02 engineering and technology 01 natural sciences 010101 applied mathematics Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Histogram Principal component analysis 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Segmentation Noise (video) Artificial intelligence 0101 mathematics business |
| Zdroj: | 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). |
| DOI: | 10.1109/wacv.2016.7477729 |
| Popis: | We propose a novel method for multi-phase segmentation of images based on high-dimensional local feature vectors. While the method was developed for the segmentation of extremely noisy crystal images based on localized Fourier transforms, the resulting framework is not tied to specific feature descriptors. For instance, using local spectral histograms as features, it allows for robust texture segmentation. The segmentation itself is based on the multi-phase Mumford-Shah model. Initializing the high-dimensional mean features directly is computationally too demanding and ill-posed in practice. This is resolved by projecting the features onto a low-dimensional space using principle component analysis. The resulting objective functional is minimized using a convexification and the Chambolle-Pock algorithm. Numerical results are presented, illustrating that the algorithm is very competitive in texture segmentation with state-of-the-art performance on the Prague benchmark and provides new possibilities in crystal segmentation, being robust to extreme noise and requiring no prior knowledge of the crystal structure. |
| Databáze: | OpenAIRE |
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