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We propose a framework for observing static scenes that can be used to detect unknown objects (i.e., left luggage or lost cargo) as well as objects that were removed or changed (i.e., theft or vandalism). The core of the method is a robust background model based on on-line AdaBoost which is able to adapt to a large variety of appearance changes (e.g.,(More)
One research direction to make computers become more active machines is to use audio input with far-field omni-directional microphones, these being required to face the acoustics problem of the meeting scenario. This topic is not only interesting from an artificial listener point-of-view, but also as one of the hottest topics in searchable media. In this(More)
BACKGROUND AND AIMS Wood density is a key variable for understanding life history strategies in tropical trees. Differences in wood density and its radial variation were related to the shade-tolerance of six canopy tree species in seasonally dry tropical forest in Thailand. In addition, using tree ring measurements, the influence of tree size, age and(More)
This paper presents a novel approach for extracting discriminative descriptions of 3-D objects using spatio-temporal information. In particular, local features are tracked in image sequences leading to local trajectories containing dynamic information. These trajectories are judged with respect to their quality and robustness and finally each of them is(More)
DESCRIPTION The main idea is to formulate the tracking problem as a binary classification task and to achieve robustness by continuously updating the current classifier of the target object with respect to the current surrounding background. For this purpose we use an on-line AdaBoost feature selection algorithm [1] for tracking. The distinct advantage of(More)