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dc.contributor.authorSiljan, Eirik
dc.date.accessioned2018-02-14T10:47:22Z
dc.date.available2018-02-14T10:47:22Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/11250/2484583
dc.description.abstractThe Combustion, Process Safety and Explosions research group at USN has collaborated with California Institute of Technology ( Calttech) and has produced high-speed videos of shock wave fronts. These videos require a framework for wave front tracking throught image processing. The framework should calculate the characteristics of the wave front, i.e. the angle and velocity of normal and oblique shocks, as well as triple point location and velocity. The framework was developed in MATLAB. It uses template matching and segmented regression to track the wave front in the high-speed videos. An extended Segmented Regression variant is developed and used with two line segments and two separate datasets where the first segment is to be fitted with the front found by the normal shock template, and the second segment is to be fitted with the points found by the oblique shock template. A "sliding window" technique is implemented to reduce the search area ot the templates. The user of the framework marks the wave front in the first and last frame. It is then possible to approximate how far the sliding window should move in each frame to follow the wave front, as well as the width of the sliding window. Once the wave front has been tracked in all frames, the velocity is calculated based on the number of pixels the normal shock, the oblique shock ant the triple point has moved in a known number of frames. The result of the applied techniques is a framework which tracks a wave front from one frame to the next. In addition, the velocity and angle oft he normal and oblique shock are presented, as well as the triple point velocity.nb_NO
dc.publisherHøgskolen i Sørøst-Norgenb_NO
dc.subjectwave frontnb_NO
dc.subjecttrackingnb_NO
dc.subjectsegmented regressionnb_NO
dc.subjecttemplate matchingnb_NO
dc.subjecttriple pointnb_NO
dc.titleWave Front Tracking using Template Matching and Segmented Regressionnb_NO
dc.typeMaster thesisnb_NO
dc.rights.holdercopyright the authornb_NO
dc.source.pagenumber49nb_NO


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