Region based Active Contour Segmentation
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.
Summary
This paper presents a general framework to convert any region-based active contour energy from a global formulation to a localized one, enabling contours to evolve using local image statistics. Readers will learn how localization improves segmentation of objects with heterogeneous feature profiles and see demonstrations that adapt three well-known energies to the local framework.
Key Takeaways
- Reformulate global region-based segmentation energies into localized versions using the authors' framework.
- Implement localized active contours that rely on local image statistics to segment heterogeneous or textured objects.
- Adapt and test three common energies (e.g., Chan–Vese and related models) within the local formulation.
- Compare localized versus global energies to assess improvements in robustness and segmentation accuracy.
Who Should Read This
Advanced graduate students, researchers, and senior engineers in image processing or computer vision who need robust region-based segmentation methods for heterogeneous or textured objects.
Still RelevantAdvanced
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