Variational Image Binarization and its Multi-Scale Realizations
Journal of Mathematical Imaging and Vision
PICASSO 2: a system for performance evaluation of image processing methods
ISCGAV'05 Proceedings of the 5th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Multispectral Image Segmentation for Fruit Quality Estimation
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
A total variation-based algorithm for pixel-level image fusion
IEEE Transactions on Image Processing
A General Bayesian Markov Random Field Model for Probabilistic Image Segmentation
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Beta-measure for probabilistic segmentation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Alpha Markov Measure Field model for probabilistic image segmentation
Theoretical Computer Science
A comparative study of image processing algorithms
Pattern Recognition and Image Analysis
"Influence areas" as a tool for testing of image restoration methods
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
Multispectral image segmentation by energy minimization for fruit quality estimation
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Comparative analysis of image processing algorithms
Pattern Recognition and Image Analysis
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A general variational framework for image approximation and segmentation is introduced. By using a continuous “line-process” to represent edge boundaries, it is possible to formulate a variational theory of image segmentation and approximation in which the boundary function has a simple explicit form in terms of the approximation function. At the same time, this variational framework is general enough to include the most commonly used objective functions. Application is made to Mumford-Shah type functionals as well as those considered by Geman and others. Employing arbitrary Lp norms to measure smoothness and approximation allows the user to alternate between a least squares approach and one based on total variation, depending on the needs of a particular image. Since the optimal boundary function that minimizes the associated objective functional for a given approximation function can be found explicitly, the objective functional can be expressed in a reduced form that depends only on the approximating function. From this a partial differential equation (PDE) descent method, aimed at minimizing the objective functional, is derived. The method is fast and produces excellent results as illustrated by a number of real and synthetic image problems