Marko Tscherepanow

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In this article we propose a novel approach to compute an image saliency map based on computing local saliencies over random rectangular regions of interest. Unlike many of the existing methods, the proposed approach does not require any training bases, operates on the image at the original scale and has only a single parameter which requires tuning. It has(More)
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)
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)
Imitating the facial expressions of another person is a meaningful signal within interpersonal communication. Providing a robot with the capability of imitating the face of an interactant marks a first step towards implementing a communication model of mimicry. In this paper, we present a novel approach to facial expression imitation which does not require(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)
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 affordances. According to the properties of biological memory systems, it has been designed in such(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)
Searching experiments conducted in different virtual environments over a gender-balanced group of people revealed a gender irrelevant scale-free spread of searching activity on large spatio-temporal scales. We have suggested and solved analytically a simple statistical model of the coherent-noise type describing the exploration-exploitation trade-off in(More)
Robots operating in complex environments shared with humans are confronted with numerous problems. One important problem is the identification of obstacles and interaction partners. In order to reach this goal, it can be beneficial to use data from multiple available sources, which need to be processed appropriately. Furthermore, such environments are not(More)
Incremental on-line learning is an important branch of machine learning. One class of approaches particularly well-suited to such tasks are Adaptive Resonance Theory (ART) neural networks. This paper presents a novel ART network combining the ability of noise-insensitive, incremental clustering and topology learning at different levels of detail from(More)