GPU-accelerated image segmentation based on level sets and multiple texture features
In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the...
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Veröffentlicht in: | Multimedia tools and applications 2021-02, Vol.80 (4), p.5087-5109 |
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description | In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms. |
doi_str_mv | 10.1007/s11042-020-09911-5 |
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subjects | Algorithms Computer Communication Networks Computer Science Contours Data Structures and Information Theory Discriminant analysis Evolution Feature extraction Gabor filters Image segmentation Methods Multimedia Multimedia Information Systems Special Purpose and Application-Based Systems Tensors Texture |
title | GPU-accelerated image segmentation based on level sets and multiple texture features |
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