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We propose an approach to multi-view object class detection and approximate 3D pose estimation. It relies on CAD models as positive training examples and discriminatively learns photometric object parts such that an optimal coverage of intra-class and viewpoint variation is guaranteed. In contrast to previous work, the approach shows a significantly reduced training set dependency while avoiding any...
We outline the retrieval of images from a network of security cameras by means of an attribute-based query. Our approach is based on detectors for several object classes which enable combined queries to retrieve people based on characteristic pieces of luggage. The approach works independently of camera recording frame rates since it does not rely on tracking or background assumptions, and it requires...
This paper presents a new approach for multi-view object class detection based on part models. While most existing approaches have in common that they use real images for training, our approach requires only a database of synthetic 3D models to represent both the appearance and the geometry of an object class. We use semantically equivalent object points on 3D models to build part models and encode...
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