A novel active contour model for unsupervised low-key image segmentation
Journal article, Peer reviewed
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Original versionMei, J., Si, Y., Karimi, H. R., & Gao, H. (2013). A novel active contour model for unsupervised low-key image segmentation. Central European Journal of Engineering, 3(2), 267-275. doi: 10.2478/s13531-012-0050-0 10.2478/s13531-012-0050-0
Unsupervised image segmentation is greatly useful in many vision-based applications. In this paper, we aim at the unsupervised low-key image segmentation. In low-key images, dark tone dominates the background, and gray level distribution of the foreground is heterogeneous. They widely exist in the areas of space exploration, machine vision, medical imaging, etc. In our algorithm, a novel active contour model with the probability density function of gamma distribution is proposed. The flexible gamma distribution gives a better description for both of the foreground and background in low-key images. Besides, an unsupervised curve initialization method is designed, which helps to accelerate the convergence speed of curve evolution. The experimental results demonstrate the effectiveness of the proposed algorithm through comparison with the CV model. Also, one real-world application based on our approach is described in this paper.
Published version of an article in the journal: Central European Journal of Engineering. Also available from the publisher at: http://dx.doi.org/10.2478/s13531-012-0050-0