Algorithms for clustering data
Algorithms for clustering data
Fundamentals of digital image processing
Fundamentals of digital image processing
Vector quantization and signal compression
Vector quantization and signal compression
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Digital image processing
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
The Cluster Dissection and Analysis Theory FORTRAN Programs Examples
Clustering Algorithms
Digital Image Processing
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acceleration of K-Means and Related Clustering Algorithms
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
Constrained Centroidal Voronoi Tessellations for Surfaces
SIAM Journal on Scientific Computing
IEEE Transactions on Image Processing
Object segmentation within microscope images of palynofacies
Computers & Geosciences
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
An edge-weighted centroidal Voronoi tessellation model for image segmentation
IEEE Transactions on Image Processing
A geometric data structure applicable to image mining and retrieval
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Generalized edge-weighted centroidal Voronoi tessellations for geometry processing
Computers & Mathematics with Applications
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Centroidal Voronoi tessellations (CVT's) are special Voronoi tessellations for which the generators of the tessellation are also the centers of mass (or means) of the Voronoi cells or clusters. CVT's have been found to be useful in many disparate and diverse settings. In this paper, CVT-based algorithms are developed for image compression, image segmenation, and multichannel image restoration applications. In the image processing context and in its simplest form, the CVT-based methodology reduces to the well-known k-means clustering technique. However, by viewing the latter within the CVT context, very useful generalizations and improvements can be easily made. Several such generalizations are exploited in this paper including the incorporation of cluster dependent weights, the incorporation of averaging techniques to treat noisy images, extensions to treat multichannel data, and combinations of the aforementioned. In each case, examples are provided to illustrate the efficiency, flexibility, and effectiveness of CVT-based image processing methodologies.