Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Active Hypercontours and Contextual Classification
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Adaptive potential active hypercontours
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Supervised Textual Document Classification Using Neuronal Group Learning
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Classification Using Geometric Level Sets
The Journal of Machine Learning Research
Contribution of hypercontours to multiagent automatic image analysis
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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In the paper we show that active contour methods can be interpreted as knowledge discovery methods. Application area is not restricted only to image segmentation, but it covers also classification of any other objects, even objects of higher granulation. Additional power of the presented method is that expert knowledge of almost any type can be used to classifier construction, which is not always possible in case of classic techniques. Moreover, the method introduced by the authors, earlier used only for supervised classification, is here applied in an unsupervised case (clustering) and examined on examples.