Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently however, the understanding which combination scheme should be used and the ability to quantify the exp...
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Location models are merely based on positional information. Using wireless sensor networks, however, allows us to extract information which can be related to different levels of semantic proximity of different devices...
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We believe that game playing in the real world finds such large appreciation because it is based on interaction between people in a physical environment. In this paper we present an example of how the gap between virt...
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We believe that game playing in the real world finds such large appreciation because it is based on interaction between people in a physical environment. In this paper we present an example of how the gap between virtual and physical games can be bridged using sensing technology from a wearable computer. Unmasking Mister X is a game we propose, which incorporates sensor data from all the players. It is a first step in enhancing real world games with wearable computers and sensing technology.
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or part-based representation of objects and images. This paper experim...
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Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently, the understanding which combination scheme should be used and the ability to quantify the expected be...
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Combining multiple classifiers promises to increase performance and robustness of a classification task. Currently, the understanding which combination scheme should be used and the ability to quantify the expected benefit is inadequate. This paper attempts to quantify the performance and robustness gain for different combination schemes and for two classifier types. The results indicate that the combination of a small number of classifiers may already result in a substantial performance gain. Also, the increase in robustness can be substantial by combining an adequate number of classifiers.
The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or part-based representation of objects and images. This paper experim...
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The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a region- or part-based representation of objects and images. This paper experimentally compares NMF to Principal Component Analysis (PCA) in the context of image patch classification. A first finding is that the two techniques are complementary and that their respective performance is correlated to the with-in class scatter. This paper also analyses different techniques to combine these complementary methods. In the first combination scheme the best technique for each class is chosen and the results are merged. The second combination scheme builds a hierarchy of classifiers where again for each classification task the best technique is chosen. Additionally, incorporation of the classification results of neighboring image patches further improves the overall results.
Even though many of today's vision algorithms are very successful, they lack robustness since they are typically limited to a particular situation. In this paper we argue that the principles of sensor and model in...
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The Perceptive Workbench endeavors to create a spontaneous and unimpeded interface between the physical and virtual worlds. Its vision-based methods for interaction constitute an alternative to wired input devices and...
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The majority of today's content based image retrieval systems rely on low-level image descriptors which limit their capability to support meaningful interactions with the users. Even though relevance feedback help...
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The majority of today's content based image retrieval systems rely on low-level image descriptors which limit their capability to support meaningful interactions with the users. Even though relevance feedback helps, most of the current interaction paradigms are far from the semantic representations which most people use to categorize and describe image content. Therefore we propose a concept called "vocabulary-supported image retrieval" which aims to enable the user to access an image database in a more natural way. In particular this paper develops a technique to predict the system's performance with respect to the user query. This allows the system to translate the user query into an internal query which may satisfy predefined criteria such as precision and recall rates. In addition, given the performance parameters of the system's sub-components, the feasibility and the success of the retrieval process can be evaluated beforehand and optimized dynamically online.
Ubiquitous computing is associated with a vision of everything being connected to everything. However, for successful applications to emerge, it will not be the quantity but the quality and usefulness of connections t...
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