A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
Preserving Topology by a Digitization Process
Journal of Mathematical Imaging and Vision
Discretization in Hausdorff Space
Journal of Mathematical Imaging and Vision
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Towards a general sampling theory for shape preservation
Image and Vision Computing
Provably correct reconstruction of surfaces from sparse noisy samples
Pattern Recognition
Contour Reconstruction for Multiple 2D Regions Based on Adaptive Boundary Samples
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Image segmentation using topological persistence
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
What can we learn from discrete images about the continuous world?
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Topologically correct 3D surface reconstruction and segmentation from noisy samples
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Region and edge-adaptive sampling and boundary completion for segmentation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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Existing theories on shape digitization impose strong constraints on feasible shapes and require error-free measurements We use Delaunay triangulation and α-shapes to prove that topologically correct segmentations can be obtained under much more realistic conditions Our key assumption is that sampling points represent object boundaries with a certain maximum error Experiments on real and generated images demonstrate the good performance and correctness of the new method.