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In the recent past, single-molecule based localization or photoswitching microscopy methods such as stochastic optical reconstruction microscopy (STORM) or photoactivated localization microscopy (PALM) have been successfully implemented for subdiffraction-resolution fluorescence imaging. However, the computational effort needed to localize numerous(More)
Memory constitutes an essential cognitive capability of humans and animals. It allows them to act in very complex, non-stationary environments. In this paper, we propose a perceptual memory system, which is intended to be applied on a humanoid robot learning affor-dances. According to the properties of biological memory systems, it has been designed in such(More)
In this paper, a novel on-line regression method is presented. Due to its origins in Adaptive Resonance Theory neural networks, this method is particularly well-suited to problems requiring stable incremen-tal learning. Its performance on five publicly available datasets is shown to be at least comparable to two established off-line methods. Furthermore ,(More)
The subcellular localisation of proteins in living cells is an important step to determine their function. A common method is the evaluation of fluorescence images. The position of marked proteins, visible as bright spots, enables conclusions concerning their function. In order to determine the subcellular localisation, it is crucial to know the exact(More)
In order to localise tagged proteins in living cells, the surrounding cells must be recognised first. Based on previous work regarding cell recognition in bright-field images, we propose an approach to the automated recognition of unstained live Drosophila cells, which are of high biological relevance. In order to achieve this goal, the original methods(More)
The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous(More)
In this article, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology-learning neural networks is presented. It enables stable on-line clustering of stationary and non-stationary input data by learning their inherent topology. Here, two network components representing two different levels of detail are trained(More)
BACKGROUND The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of(More)