Applied multivariate statistical analysis
Applied multivariate statistical analysis
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiscale algorithm for image segmentation by variational method
SIAM Journal on Numerical Analysis
Variational methods in image segmentation
Variational methods in image segmentation
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Robust Segmentation of Natural Color Scenes
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Segmenting Images Using Localized Histograms and Region Merging
Segmenting Images Using Localized Histograms and Region Merging
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACM SIGGRAPH 2004 Papers
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Semi-Supervised Classification Using Linear Neighborhood Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robotics: Science and Systems II
Robotics: Science and Systems II
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Color image segmentation guided by a color gradient network
Pattern Recognition Letters
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Short communication: An evaluation metric for image segmentation of multiple objects
Image and Vision Computing
International Journal of Computer Vision
Color image segmentation using an enhanced Gradient Network Method
Pattern Recognition Letters
Learning a color distance metric for region-based image segmentation
Pattern Recognition Letters
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Image segmentation evaluation by techniques of comparing clusterings
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
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In this paper a novel region-merging image segmentation approach is presented. This approach is based on a two-step procedure: a distance metric is learned from some features on the image, then a piecewise approximation function for the Mumford-Shah model is optimized by this metric. The global optimum of the approximation function is inductively achieved under high polynomial terms of the Mahalanobis distance, extracting the nonlinear features of the pattern distributions into topological maps. The penalizer terms of the Mumford-Shah equation are based on new similarity criteria, computed from the topological maps and the class label information. The results we obtained show a better discrimination of object boundaries and the location of regions when compared with the conventional Mumford-Shah algorithm, even when supplied with other well-known similarity functions. A quantitative objective evaluation of the proposed approach was performed in order to compute the quality of the obtained results.