A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
The union of balls and its dual shape
SCG '93 Proceedings of the ninth annual symposium on Computational geometry
Preserving Topology by a Digitization Process
Journal of Mathematical Imaging and Vision
Euclidean paths: a new representation of boundary of discrete regions
Graphical Models and Image Processing
Discretization in Hausdorff Space
Journal of Mathematical Imaging and Vision
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Computer and Robot Vision
Subpixel-Precise Extraction of Watersheds
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Correction for the Dislocation of Curved Surfaces Caused by the PSF in 2D and 3D CT Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Towards a general sampling theory for shape preservation
Image and Vision Computing
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
A new sub-pixel map for image analysis
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Pixel approximation errors in common watershed algorithms
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
On the search of optimal reconstruction resolution
Pattern Recognition Letters
WαSH: weighted α-shapes for local feature detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Crypts detection in microscopic images using hierarchical structures
Pattern Recognition Letters
Smoothness of Boundaries of Regular Sets
Journal of Mathematical Imaging and Vision
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Existing theories on shape digitization impose strong constraints on admissible shapes, and require error-free data. Consequently, these theories are not applicable to most real-world situations. In this paper, we propose a new approach that overcomes many of these limitations. It assumes that segmentation algorithms represent the detected boundary by a set of points whose deviation from the true contours is bounded. Given these error bounds, we reconstruct boundary connectivity by means of Delaunay triangulation and @a-shapes. We prove that this procedure is guaranteed to result in topologically correct image segmentations under certain realistic conditions. Experiments on real and synthetic images demonstrate the good performance of the new method and confirm the predictions of our theory.