Klaus Häming

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The problem addressed in this paper is the reconstruction of an object in the form of a realistically textured 3D model from images taken with an uncalibrated camera. We especially focus on reconstructions from short image sequences. By means of a description of an easy to use system, which is able to accomplish this in a fast and reliable way, we give a(More)
We discuss the use of Ordinal Conditional Functions (OCF) in the context of Reinforcement Learning while introducing a new revision operator for conditional information. The proposed method is compared to the state-of-the-art method in a small Reinforcement Learning application with added futile information, where generalization proves to be advantageous.
Large state spaces pose a serious problem in many learning applications. This paper discusses a number of issues that arise when ranking functions are applied to such a domain. Since these functions, in their original introduction, need to store every possible world model, it seems obvious that they are applicable to small toy problems only. To disprove(More)
This paper addresses the topic of image rectification, a widely used technique in 3D-reconstruction and stereo vision. The most popular algorithm uses a projective transformation to map the epipoles of the images to infinity. This algorithm fails whenever an epipole lies inside an image. To overcome this drawback, a rectification scheme known as polar(More)
To enable a reinforcement learning agent to acquire symbolical knowledge, we augment it with a high-level knowledge representation. This representation consists of ordinal conditional functions (OCF) which allow it to rank world models. By this means the agent is enabled to complement the self-organizing capabilities of the low-level reinforcement learning(More)
We introduce a tool which allows an untrained user to take three images of an object freehand with a simple consumer camera. From these images a 3d model of the visible parts of the object is reconstructed within seconds and visualized realistically. From a research point of view we propose solutions for three weaknesses of the state-of-the-art(More)
We propose a combination of belief revision and reinforcement learning which leads to a self-learning agent. The agent shows six qualities we deem necessary for a successful and adaptive learner. This is achieved by representing the agent’s belief in two different levels, one numerical and one symbolical. While the former is implemented using basic(More)