Worst-case Analysis of Set Union Algorithms
Journal of the ACM (JACM)
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Connected Component Labeling Using Quadtrees
Journal of the ACM (JACM)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Communications of the ACM
A hierarchical data structure for multidimensional digital images
Communications of the ACM
An effective way to represent quadtrees
Communications of the ACM
Efficient octree conversion by connectivity labeling
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Efficient computation and data structures for graphics.
Efficient computation and data structures for graphics.
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Hierarchical Data Structures and Algorithms for Computer Graphics. Part I.
IEEE Computer Graphics and Applications
Hierarchical representations of collections of small rectangles
ACM Computing Surveys (CSUR)
A general approach to connected-component labeling for arbitrary image representations
Journal of the ACM (JACM)
Parallel Architectures and Algorithms for Image Component Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstructing multishell solids from voxel-based contours
SMA '93 Proceedings on the second ACM symposium on Solid modeling and applications
Parallel Image Component Labeling With Watershed Transformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Navigating through triangle meshes implemented as linear quadtrees
ACM Transactions on Graphics (TOG)
Hierarchical Data Structures and Algorithms for Computer Graphics
IEEE Computer Graphics and Applications
Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Object-based and image-based object representations
ACM Computing Surveys (CSUR)
Fast connected-component labelling in three-dimensional binary images based on iterative recursion
Computer Vision and Image Understanding
Strongly normal sets of contractible tiles in N dimensions
Pattern Recognition
Fast connected-component labeling
Pattern Recognition
Fast connected-component labelling in three-dimensional binary images based on iterative recursion
Computer Vision and Image Understanding
Real-Time Object-Based Video Segmentation Using Colour Segmentation and Connected Component Labeling
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Multidimensional data structures for spatial applications
Algorithms and theory of computation handbook
Combined MR brain segmentation
Annales UMCS, Informatica
Combined MR brain segmentation
Annales UMCS, Informatica
Embroidery modeling and rendering
Proceedings of Graphics Interface 2012
Data-parallel mesh connected components labeling and analysis
EG PGV'11 Proceedings of the 11th Eurographics conference on Parallel Graphics and Visualization
Transforming cluster-based segmentation for use in OpenVL by mainstream developers
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Large scale continuous visual event recognition using max-margin Hough transformation framework
Computer Vision and Image Understanding
Efficient algorithm for automatic road sign recognition and its hardware implementation
Journal of Real-Time Image Processing
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An algorithm is presented to perform connected-component labeling of images of arbitrary dimension that are represented by a linear bintree. The bintree is a generalization of the quadtree data structure that enables dealing with images of arbitrary dimension. The linear bintree is a pointerless representation. The algorithm uses an active border which is represented by linked lists instead of arrays. This results in a significant reduction in the space requirements, thereby making it feasible to process three- and higher-dimensional images. Analysis of the execution time of the algorithm shows almost linear behavior with respect to the number of leaf nodes in the image, and empirical tests are in agreement. The algorithm can be modified easily to compute a (d-1)-dimensional boundary measure (e.g. perimeter in two dimensions and surface area in three dimensions) with linear performance.