Johannes Schels

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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(More)
This paper proposes a novel approach to multi-view object class and viewpoint detection for the retrieval of images showing one or several objects from a given viewpoint, a viewpoint range or any viewpoint in image databases. All detectors are trained exclusively on a few synthetic 3D models without any manual bounding-box, viewpoint or part annotation,(More)
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(More)
In this paper we present 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(More)
This thesis presents part-based approaches to object class detection in single 2D images, relying on prebuilt CAD models as a source of synthetic training data. Part-based models, representing an object class as a deformable constellation of object parts, have demonstrated state-of-the-art results with respect to object class detection. Typically, the(More)
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