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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Ingo Fründ,
"Speed in early visual processing",
Universität Magdeburg, 2007.
Abstract
The ability to recognize objects rapidly and reliably is crucuial for the functioning of the visual system. Such rapid processing could be implemented by phase resets of high frequency signals that are propagated through the visual system. Such signals were investigated using time-frequency analyses of electroencephalogram time series. I demonstrate that such high frequency signals can reliably be measured and proceed to report two experiments th...
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Julian Eggert, Chen Zhang, Edgar Körner,
"Template matching for large transformations",
Artificial Neural Networks, 17. International Conference (ICANN), pp. 169-179, 2007.
Abstract
Finding a template image in another larger image is a problem that has applications in many vision research areas such as models for object detection and tracking. The main problem here is that under real-world conditions the searched image usually is a deformed version of the template, so that these deformations have to be taken into account by the matching procedure. A common way to do this is by minimizing the difference between the template a...
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Dudy Lim, Yew-Soon Ong, Meng-Hiot Lim, Yaochu Jin,
"Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty",
Evolutionary Computation in Dynamic and Uncertain Environments, Springer, 2007.
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Mark Toussaint and Christian Goerick,
"Probabilistic inference for structured planning in robotics",
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), 2007.
Abstract
Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially depends on exploiting this structure. We propose a new approach to planning in robotics based on probabilistic inference. The method uses structured Dynamic Bayesian Networks to represent the scenario and efficient inference techniques (loopy belief propagation) to solve planning pro...
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