Abstract
This paper studies image object coverage segmentation by introducing soft boundaries. By using soft boundaries, fuzzy image can be segmented into several classes with a sharing boundary which is called a soft boundary. In this paper, several concepts of boundaries are defined, namely, hard boundary, inner boundary and outer boundary. Soft boundary is defined by the subtraction between inner boundary and outer boundary of a set. Coverage segmentation algorithm and optimization method are proposed in this paper. Meanwhile, neighbor decision rules are used in classification of pixels to filter noise or outliers. Experiments and comparison with classical coverage segmentation methods are presented, including noise test on the proposed method with four kinds of boundaries and neighbor decision rules.
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Liang, J., Gu, Y., Di, L., Wu, Q. (2014). Image Coverage Segmentation Based on Soft Boundaries. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds) Rough Sets and Current Trends in Computing. RSCTC 2014. Lecture Notes in Computer Science(), vol 8536. Springer, Cham. https://doi.org/10.1007/978-3-319-08644-6_39
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DOI: https://doi.org/10.1007/978-3-319-08644-6_39
Publisher Name: Springer, Cham
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