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)
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)
—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)
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)
Object recognition and localization methods in RoboCup work on color-segmented camera images. Unfortunately, color labeling can be applied to object-recognition tasks only in very restricted environments, where different kinds of objects have different colors. To overcome these limitations , we propose an algorithm, called the CONTRACTING CURVE DENSITY(More)
In many autonomous robot applications robots must be capable of estimating the positions and motions of moving objects in their environments. In this paper, we apply probabilistic multiple object tracking to estimating the positions of opponent players in autonomous robot soccer. We extend an existing tracking algorithm to handle multiple mobile sensors(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 prob-abilistic game state estimator and a situated action selection module. The game state estimator computes the robot's(More)
This paper describes the Agilo RoboCuppers 1 { the RoboCup team of the image understanding group (FG BV) at the Technische Uni-versitt at M unchen. With a team of ve Pioneer 1 robots, equipped with CCD camera and a single board computer each and coordinated by a master PC outside the eld we participate in the Middle Robot League of the Third International(More)