A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust method for picture segmentation based on split-and-merge procedure
Computer Vision, Graphics, and Image Processing
Visual reconstruction
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern Recognition
On active contour models and balloons
CVGIP: Image Understanding
Split-and-merge image segmentation based on localized feature analysis and statistical tests
CVGIP: Graphical Models and Image Processing
A new approach for multilevel threshold selection
CVGIP: Graphical Models and Image Processing
Region-based strategies for active contour models
International Journal of Computer Vision
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Computer and Robot Vision
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corrections to "General Scheme of Region Competition Based on Scale Space"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Joint brain parametric T1-map segmentation and RF inhomogeneity calibration
Journal of Biomedical Imaging
Multiphase joint segmentation-registration and object tracking for layered images
IEEE Transactions on Image Processing
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In this paper, we propose a general scheme of region competition (GSRC) for image segmentation based on scale space. First, we present a novel classification algorithm to cluster the image feature data according to the generally defined peaks under a certain scale and a scale space-based classification scheme to classify the pixels by grouping the resultant feature data clusters into several classes with a standard classification algorithm. Second, to reduce the resultant segmentation error, we develop a nonparametric probability model from which the functional for GSRC is derived. Third, we design a general and formal approach to automatically determine the initial regions. Finally, we propose the kernel procedure of GSRC which segments an image by minimizing the functional. The strategy adopted by GSRC is first to label pixels whose corresponding regions can be determined in large likelihood, and then to fine-tune the final regions with the help of the nonparametric probability model, boundary smoothing, and region competition. GSRC quantitatively controls the segmentation extent with the scale space-based classification scheme. Although the description of the scheme is nonparametric in this paper, GSRC can also work parametrically if all nonparametric procedures in this paper are substituted with the parametric counterparts.