Joerg Liebelt

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This paper presents a new approach for multi-view object class detection. Appearance and geometry are treated as separate learning tasks with different training data. Our approach uses a part model which discriminatively learns the object appearance with spatial pyramids from a database of real images, and encodes the 3D geometry of the object class with a(More)
This paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set(More)
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)
Active Appearance Models (AAMs) have been popularly used to represent the appearance and shape variations of human faces. Fitting an AAM to images recovers the face pose as well as its deformable shape and varying appearance. Successful fitting requires that the AAM is sufficiently generic such that it covers all possible facial appearances and shapes in(More)
The precise alignment of a 3D model to 2D sensor images to recover the pose of an object in a scene is an important topic in computer vision. In this work, we outline a registration scheme to align arbitrary standard 3D models to optical and synthetic aperture radar (SAR) images in order to recover the full 6 degrees of freedom of the object. We propose a(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)
Figure 1: Humans are perfect for annotating coarse viewpoints of objects in real images, but fail to estimate pose accurately at a fine level. 3D graphic models can be used to synthesize data at very accurate fine angles, but it is time-consuming to model all appearance variations present in real images. We therefore propose to leverage the abilities of(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)
The present thesis describes 3D model-based approaches to object class detection and pose estimation on single 2D images. We introduce learning, detection and estimation steps adapted to the use of synthetically rendered training data with known 3D geometry. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers(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)
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