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Localizing Region-Based Active Contours
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AI
DOI：10.1109/TIP.2008.2004611.png)
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Abstract
En 中文
In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models.
Keywords:
Active contours
level set methods
curve evolution
image segmentation
partial differential equations
multiregion segmentation
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
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