Learning to Detect Windows in Urban Environments

Haider Ali, Christin Seifert, Nitin Jindal, Lucas Paletta, Gerhard Paar

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    This work is about a novel methodology for window detection in urban environments and its multiple use in vision system applications. The presented method for window detection includes appropriate early image processing, provides a multi-scale Haar wavelet representation for the determination of image tiles which is then fed into a cascaded classifier for the task of window detection. The classifier is learned from a Gentle Adaboost driven cascaded decision tree [1] on masked information from training imagery and is tested towards window based ground truth information which is - together with the original building image databases - publicly available [9, 10, 12]. The experimental results demonstrate that single window detection is to a sufficient degree successful, e.g. for the purpose of building recognition, and, furthermore, that the classifier is in general capable to provide a region of interest operator for the interpretation of urban environments. The extraction of this categorical information is beneficial to index into search spaces for urban object recognition as well as aiming towards providing a semantic focus for accurate post-processing in 3D information processing systems. Targeted applications are (i) mobile services on uncalibrated imagery, e.g. for tourist guidance, (ii) sparse 3D city modeling, and (iii) deformation analysis from high resolution imagery.
    Original languageEnglish
    Title of host publicationProceedings of the 31st Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR)
    PublisherAustrian Association for Pattern Recognition (AAPR)
    Number of pages8
    Publication statusPublished - 2007
    Event31st Annual Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) 2007 - Schloss Krumbach, Krumbach, Austria
    Duration: 3 May 20074 May 2007
    Conference number: 31


    Conference31st Annual Workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) 2007


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