Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
Hexagonal Image Processing: A Practical Approach (Advances in Pattern Recognition)
Discrete quadratic curvature energies
Computer Aided Geometric Design
Global Stereo Reconstruction under Second-Order Smoothness Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient surface completion using principal curvature and its evaluation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Curvature regularity for multi-label problems - standard and customized linear programming
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Disocclusion: a variational approach using level lines
IEEE Transactions on Image Processing
Curvature regularity for multi-label problems - standard and customized linear programming
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
International Journal of Computer Vision
Discrete Applied Mathematics
Tighter relaxations for higher-order models based on generalized roof duality
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Robust and efficient object segmentation using pseudo-elastica
Pattern Recognition Letters
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Length and area regularization are commonplace for inverse problems today. It has however turned out to be much more difficult to incorporate a curvature prior. In this paper we propose several improvements to a recently proposed framework based on global optimization. We identify and solve an issue with extraneous arcs in the original formulation by introducing region consistency constraints. The mesh geometry is analyzed both from a theoretical and experimental viewpoint and hexagonal meshes are shown to be superior. We demonstrate that adaptively generated meshes significantly improve the performance. Our final contribution is that we generalize the framework to handle mean curvature regularization for 3D surface completion and segmentation.