Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute openings, thinnings, and granulometries
Computer Vision and Image Understanding
An improved seeded region growing algorithm
Pattern Recognition Letters
Connectivity on Complete Lattices
Journal of Mathematical Imaging and Vision
Interactive segmentation with Intelligent Scissors
Graphical Models and Image Processing
Connected filtering and segmentation using component trees
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Preserving Filament Enhancement Filtering
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Tree Representation for Image Matching and Object Recognition
DCGI '99 Proceedings of the 8th International Conference on Discrete Geometry for Computer Imagery
Efficient Algorithms to Implement the Confinement Tree
DGCI '00 Proceedings of the 9th International Conference on Discrete Geometry for Computer Imagery
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A main stem concept for image matching
Pattern Recognition Letters
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mask-Based Second-Generation Connectivity and Attribute Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-Based Image Retrieval in Binary Image Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Complex Images Based on Component-Trees: Methodological Tools
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Component-Trees and Multi-value Images: A Comparative Study
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
Fast fuzzy connected filter implementation using max-tree updates
Fuzzy Sets and Systems
A document binarization method based on connected operators
Pattern Recognition Letters
An extension of component-trees to partial orders
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Attribute-filtering and knowledge extraction for vessel segmentation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Antiextensive connected operators for image and sequence processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Building the Component Tree in Quasi-Linear Time
IEEE Transactions on Image Processing
Volumetric Attribute Filtering and Interactive Visualization Using the Max-Tree Representation
IEEE Transactions on Image Processing
Component-hypertrees for image segmentation
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Revisiting component tree based segmentation using meaningful photometric informations
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Knot segmentation in noisy 3d images of wood
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Component-Trees and Multivalued Images: Structural Properties
Journal of Mathematical Imaging and Vision
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Component-trees associate to a discrete grey-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an original interactive segmentation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the grey-level structure of the image) a given binary target selected beforehand in the image. A proof of the algorithmic efficiency of this methodological scheme is proposed. Concrete application examples on magnetic resonance imaging (MRI) data emphasise its actual computational efficiency and its usefulness for interactive segmentation of real images.