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Towards multi-stage texture-based active contour image segmentation

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Abstract

In this paper, we present a three-stage approach to incorporation of texture analysis into a two-dimensional active contour segmentation framework. This approach allows to utilise texture information alongside other image features. The proposed method starts with an initial unsupervised feature computation and selection, then moves to a fast contour evolution process and ends with a final refinement stage. The algorithm is designed to be general in its nature and not restricted to any particular texture feature extraction method. In this paper, the initial stage generates a set of feature maps consisting of grey-level co-occurrence matrix and Gabor features. The implementation makes an extensive use of hardware acceleration for efficient calculation of a relatively large number of features. The performance of the method was tested on various synthetic and natural images and compared with results of other algorithms.

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Acknowledgements

This work was supported by Bialystok University of Technology under Grants W/WI/1/2016 and S/WI/2/2013.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Daniel Reska, Cezary Boldak & Marek Kretowski

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  1. Daniel Reska
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  2. Cezary Boldak
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  3. Marek Kretowski
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Correspondence to Daniel Reska.

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Reska, D., Boldak, C. & Kretowski, M. Towards multi-stage texture-based active contour image segmentation. SIViP 11, 809–816 (2017). https://doi.org/10.1007/s11760-016-1026-y

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  • DOI: https://doi.org/10.1007/s11760-016-1026-y

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