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Institute
In my research sabbatical I was working on three different topics, namely orthogonal polynomials in geometric modeling, re-parametrized univariate subdivision curves, and reconstruction of 3d-fish-models and other zoological artifacts. In the subsequent Sections, I will describe my particular activity in these different fields. The sections are meant to present an overview of my research activities, leaving out the technical details.
Section 1 is on orthogonal polynomials and other related generating systems for functions systems of smooth function.
In Section 2, I will discuss the application of various re-parametrization schemes for interpolatory subdivision algorithms for the generation of space curves.
The next Section 3 is concerned with my research at the University of Queensland, Brisbane, in collaboration with Dr. Ulrike Siebeck from the School of Biomedical Sciences on fish behavior and reconstruction of 3d-fish models in particular.
In the last Section 4, I will describe what effects this research will have on in my subsequent teaching at the University of Applied Science Konstanz (HTWG).
The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense by hand the entire underwater infrastructure. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose to scan the above and underwater port structure with a Multi-SensorSystem, and -by a fully automated processto classify the obtained point cloud into damaged and undamaged zones. We make use of simulated training data to test our approach since not enough training data with corresponding class labels are available yet. To that aim, we build a rasterised heightfield of a point cloud of a sheet pile wall by cutting it into verticall slices. The distance from each slice to the corresponding line generates the heightfield. This latter is propagated through a convolutional neural network which detects anomalies. We use the VGG19 Deep Neural Network model pretrained on natural images. This neural network has 19 layers and it is often used for image recognition tasks. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection which is able to analyse the whole structure instead of the sample wise manual method with divers. The mean true positive rate is 0.98 which means that we detected 98 % of the damages in the simulated environment.
Three-dimensional ship localization with only one camera is a challenging task due to the loss of depth information caused by perspective projection. In this paper, we propose a method to measure distances based on the assumption that ships lie on a flat surface. This assumption allows to recover depth from a single image using the principle of inverse perspective. For the 3D ship detection task, we use a hybrid approach that combines image detection with a convolutional neural network, camera geometry and inverse perspective. Furthermore, a novel calculation of object height is introduced. Experiments show that the monocular distance computation works well in comparison to a Velodyne lidar. Due to its robustness, this could be an easy-to-use baseline method for detection tasks in navigation systems.