Robert Hanek

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The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density (CCD) algorithm as a solution to the(More)
With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this article, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a(More)
An accurate reduction poses little difficulty for arguments of a few radians. However for, say, a CRAY1, H format on the VAX, or double extended in the proposed IEEE standard, the maximum argument which might be presented for reduction is of the order of 2^16000 radians. Accurate reduction of such an argument would require storage of π (or its(More)
In many robot applications, autonomous robots must be capable of localizing the objects they are to manipulate. In this paper we address the object localization problem by fitting a parametric curve model to the object contour in the image. The initial prior of the object pose is iteratively refined to the posterior distribution by optimizing the separation(More)
This article describes the computational model underlying the AGILO autonomous robot soccer team, its implementation, and our experiences with it. According to our model the control system of an autonomous soccer robot consists of a probabilistic game state estimator and a situated action selection module. The game state estimator computes the robot’s(More)
In this paper we address the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge local statistics are learned for both sides of the edge. These(More)
This paper presents the vision system of the robot soccer team Agilo RoboCuppers 1 { the RoboCup team of the image understanding group (FG BV) at the Technische Universitt at M unchen. We present a fast and robust color classiication method yielding signii-cant regions in the image. The boundaries between adjacent regions are used to localize objects like(More)
The approach presented in this paper allows a team of mobile robots to estimate cooperatively their poses, i.e. positions and orientations, and the poses of other observed objects from images. The images are obtained by calibrated color cameras mounted on the robots. Model knowledge of the robots' environment, the geometry of observed objects, and the(More)