The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Consistency and stability of active contours with Euclidean and non-Euclidean arc lengths
IEEE Transactions on Image Processing
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The geometric deformable model (GDM) provides a useful framework for segmentation by integrating the energy minimization concept of classical snakes with the topologically flexible gradient flow. The key aspect of this technique is the image derived conformal metric for the configuration space. While the theoretical and numerical aspects of the geometric deformable model have been discussed in the literature, the formation of the conformal metric itself has not received much attention. Previous definitions of the conformal metric do not allow the GDM to produce reliable segmentation results in low-contrast or highblur regions. This paper examines the desired properties of the conformal metric with regard to the image information and proposes an elliptic partial differential equation to construct the metric. Our method produces similar results to other metric definitions in high-contrast regions, but produces better results in low-contrast, high-blur situations.