Monocular Depth Estimation of micro scale Images

dc.contributor.advisorKietzmann, Tim
dc.contributor.advisorBoughorbel, Faysal
dc.contributor.authorStracke, Nick
dc.description.abstractDepth estimation based on a single image is a hot topic in current computer vision and deep learning research. Current work almost exclusively uses data depicting street or indoor scenes to train and benchmark their methods. However, there are plenty of other domains where depth information can be useful. I will elaborate how current state of the art methods can be applied to the domain of semiconductor devices where depth information can be useful to assess whether a device is damaged or faulty. I will showcase the entire process starting with a pipeline to obtain ground truth data and ending with bench marking several models. More precisely, I will train and evaluate a supervised, a self supervised and a semi supervised neural network. Ultimately, I demonstrate that it is possible to extract some depth information but that more research and data is needed to achieve dense and accurate disparity maps.en_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleMonocular Depth Estimation of micro scale Imagesen_US
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