Christian Igel and Bernhard Sendhoff,
"Genesis of organic computing systems: Coupling evolution and learning",
Organic Computing, pp. 141–166, 2008.
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Andreas Knoblauch,
"Symbols and embodiment from the perspective of a neural modeller.",
Symbols and Embodiment: Debates on Meaning and Cognition., Oxford University Press, pp. 117–143, 2008.
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Andreas Knoblauch, Friedrich Sommer, Marc-Oliver Gewaltig, Rüdiger Kupper, Ursula Körner, Edgar Körner,
"On the collective computational abilities of inhibitory neurons.",
Proceedings of the 5th Computational and Systems Neuroscience Meeting (COSYNE), pp. 160, 2008.
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Alexander Denecke, Heiko Wersing, Jochen Steil, Edgar Körner,
"Robust object segmentation by adaptive metrics in Generalized LVQ",
Proceedings of the European Symposium on Artificial Neural Networks (ESANN), pp. 319–324, 2008.
Abstract
We investigate the effect of several adaptive metrics in the context of figure-ground segregation, using Generalized LVQ to train a classifier for image regions. Extending the Euclidean metrics towards local matrices of relevance-factors does not only lead to a higher classification accuracy and increased robustness on heterogeneous/noisy data, but also figureground segregation using this adaptive metrics enables a considerably higher recognition...
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Alexander Gepperth, Jannik Fritsch, Christian Goerick,
"Cross-module learning as a first step towards a cognitive system concept",
Proceedings of the First International Conference on Cognitive Systems, 2008.
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Stephan Kirstein, Heiko Wersing, Edgar Körner,
"A biologically motivated visual memory architecture for online learning of objects",
Neural Networks, vol. 21, no. 1, pp. 65–77, 2008.
Abstract
We present a biologically motivated architecture for object recognition that is based on a hierarchical feature-detection model in combination with a memory architecture that implements short-term and long-term memory for objects. A particular focus is the functional realization of online and incremental learning for the task of appearance-based object recognition of many complex-shaped objects. We propose some modifications of learning vector qu...
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Sven Rebhan, Waqas Sharif, Julian Eggert,
"Incremental Learning in the Non-negative Matrix Factorization",
Proceedings of the 15th International Conference on Neural Information Processing, pp. 960-969, 2008.
Abstract
The non-negative matrix factorization (NMF) is capable of factorizing strictly positive data into strictly positive activations and base vectors. In its standard form, the input data must be presented as a batch of data. This means the NMF is only able to represent the input space contained in this batch of data whereas it is not able to adapt to changes afterwards. In this paper we propose a method to overcome this limitation and to enable the N...
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Daniel Weiler and Julian Eggert,
"Multi-Dimensional Histogram-Based Image Segmentation",
Proceedings of the 14th International Conference on Neural Information Processing (ICONIP), 2007.
Abstract
In this paper we present an approach for multi-dimensional histogram-based image segmentation. We combine level-set methods for image segmentation with probabilistic region descriptors based on multi- dimensional histograms. Unlike stated by other authors we show that colour space histograms provide a reasonable and efficient description of image regions. In contrast to Gaussian Mixture Model based algorithms no parameter learning and estimation ...
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Mark Toussaint, Volker Willert, Julian Eggert, Edgar Körner,
"Motion Segmentation Using Inference in Dynamic Bayesian Networks",
Proceedings of the 18th British Machine Vision Conference (BMVC), 2007.
Abstract
Existing formulations for optical flow estimation and image segmentation have used Bayesian Networks and Markov Random Field (MRF) priors to impose smoothness of segmentation. These approaches typically focus on estimation in a single time slice based on two consecutive images. We develop a motion segmentation framework for a continuous stream of images using inference in a corresponding Dynamic Bayesian Network (DBN) formulation. It realises a s...
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Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, Edward Tsang,
"Prediction-based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization",
The Fourth International Conference on Evolutionary Multi-Criterion Optimization, pp. 832–846, 2007.
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