Shift reducing of retinal vessel image series by using edge based template matching algorithm

This paper presents a shift reducing algorithm of fundus images consisting of the following steps: edge detection by a rule based gradient algorithm, algorithm to search significant templates and a template matching algorithm based on edge lists. The result of the shift reducing algorithm depends on the capability of the edge detection algorithm and the detected templates. The shift reducing algorithm works well and effectively

Vergleich kantenlistenbasierter Bildmatchingverfahren zur Bewegungskompensation von Fundusbildern

This paper prescnts different approaches on edge hascd template matching algorithms. The result of the edge based template matching algorithms are compared with common template matching algorithms. Theretfore the correlation between different templates and artifical images with different noise, brightness and contrast were calculated. The results of the different algorithms were compared using the peak signal-to-noise ratio (PSNR)

Methode zur Untersuchung des dynamischen Verhaltens von Netzhautgefässen

This paper offers various approches to destine the changing of the diameter of human relatinal vessel. The focus is rather on the approches than on the physiological discussion of the results. The source signal has particular properties thus a costly signal preprocessing is necessary. This approches will be explained and discussed. The final transformation in the freqency domain is based on a common DFT algorithm. The calculated results will be compared

Collaborative multi-scale 3D city and infrastructure modeling and simulation

Computer-aided collaborative and multi-scale 3D planning are challenges for complex railway and subway track infrastructure projects in the built environment. Many legal, economic, environmental, and structural requirements have to be taken into account. The stringent use of 3D models in the different phases of the planning process facilitates communication and collaboration between the stake holders such as civil engineers, geological engineers, and decision makers. This paper presents concepts, developments, and experiences gained by an interdisciplinary research group coming from civil engineering informatics and geo-informatics banding together skills of both, the Building Information Modeling and the 3D GIS world. New approaches including the development of a collaborative platform and 3D multi-scale modelling are proposed for collaborative planning and simulation to improve the digital 3D planning of subway tracks and other infrastructures. Experiences during this research and lessons learned are presented as well as an outlook on future research focusing on Building Information Modeling and 3D GIS applications for cities of the future

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks