Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A fast level set method for propagating interfaces
Journal of Computational Physics
International Journal of Computer Vision
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Image Segmentation by Unifying Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Integration of Image Segmentation Maps using Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images.