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
Hauptverfasser: Reska, Daniel, Kretowski, Marek
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09911-5