Artificial Neural Networks: Biological Inspirations – ICANN 2005

Artificial Neural Networks: Biological Inspirations – ICANN 2005

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The two volume set LNCS 3696 and LNCS 3697 constitutes the refereed proceedings of the 15th International Conference on Artificial Neural Networks, ICANN 2005, held in Warsaw, Poland in September 2005. The over 600 papers submitted to ICANN 2005 were thoroughly reviewed and carefully selected for presentation. The first volume includes 106 contributions related to Biological Inspirations; topics addressed are modeling the brain and cognitive functions, development of cognitive powers in embodied systems spiking neural networks, associative memory models, models of biological functions, projects in the area of neuroIT, evolutionary and other biological inspirations, self-organizing maps and their applications, computer vision, face recognition and detection, sound and speech recognition, bioinformatics, biomedical applications, and information- theoretic concepts in biomedical data analysis. The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.This paper presents a novel approach for image segmentation with the fusion of morphological watershed transform(WST) and feedback pulse coupled ... The complementary advantage and limitation of WST and PCNN imply a promising direction to fuse these two methods together for image segmentation. ... WST for image segmentation is based on the topographic interpretation of a grey scale image.

Title:Artificial Neural Networks: Biological Inspirations – ICANN 2005
Author:Wlodzislaw Duch
Publisher:Springer Science & Business Media - 2005


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