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Place recognition is a fundamental requirement for mobile robots. It is particularly needed for detecting loop closures in SLAM and to enable self-localization for mobile robots given a prior map. The multitude of existing approaches rely on appearance based methods, e.g. the extraction of interest points in terms of local extrema. It can be observed that the availability of these features is highly...
The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w.r.t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance...
Frequencies of occurrence of low-level image features is the representation of choice in the design of state-of-the-art visual object recognition systems. A crucial step in this process is the construction of a codebook of visual features, which is usually done by cluster analysis of a large number of low-level image features detected as interest points. However, clustering is a process that retains...
According to the strong relationship between low level features in an image and human emotion, an approach to classify wood images into emotional categories(gorgeous vs. simple) is proposed in this paper. Through analyzing the wood texture feature by the viewpoint of visual perception and image analysis, some features are structured to depict the relationship between wood texture and human emotional...
Video summarization is not only the key to effective cataloging and browsing video, but also as an embedded cue to trace video object activities. In this paper, a video summarization approach based on machine learning is developed for automatic video transition prediction. Several novel features are extracted to characterize video boundary, including cut, fade in, fade out and dissolve for facilitating...
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