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• W2736637269 abstract "Image segmentation based on active contour model has been widely used in recent years. However, in real application, image segmentation results are always leaded to corrupt because of partial missing of target boundaries or some misleading factors. In order to solve the issue, Zhou proposed a model called active contour with group similarity. But Zhou’s model is purely based on global information of image and cannot deal with image segmentation with intensity inhomogeneity. In order to optimize Zhou’s model and make it more robust, we construct a new energy function which combines global feature with local feature of image. The local feature can solve the issue of image segmentation with intensity inhomogeneity. And the global feature can make our model less sensitive to the initial position of the curve or noise. The experiment results have proved that, our method can achieve satisfying segmentation results on image segmentation with intensity inhomogeneity and show less sensitive to the initial position of the curve." @default.
• W2736637269 created "2017-07-31" @default.
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• W2736637269 date "2017-01-01" @default.
• W2736637269 modified "2023-09-26" @default.
• W2736637269 title "A More Robust Active Contour Model with Group Similarity" @default.
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• W2736637269 doi "https://doi.org/10.1007/978-3-319-63309-1_38" @default.
• W2736637269 hasPublicationYear "2017" @default.
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