Image segmentation is an important process in image processing. Clustering-based image segmentation algorithms have a number of advantages such as continuous contour and non-threshold. However, most of the clustering-based image segmentation algorithms may occur an oversegmentation problem or need numerous control parameters depending on image. In this paper, a non-parametric clustering-based image segmentation algorithm using an orthogonal experimental design based hill-climbing is proposed. For solving the oversegmentation problem, a general-purpose evaluation function is used in the algorithm. Experimental results of natural images demonstrate the effectiveness of the proposed algorithm. © Springer-Verlag 2003.
CITATION STYLE
Lee, K. Z., Chuang, W. C., & Ho, S. Y. (2004). A non-parametric image segmentation algorithm using an orthogonal experimental design based hill-climbing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2690, 1076–1081. https://doi.org/10.1007/978-3-540-45080-1_154
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