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| + | {{page> | ||
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| + | ====== Now at MIT ====== | ||
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| + | **I have moved** to the [[http:// | ||
| + | |||
| + | ====== Brief Bio ====== | ||
| + | |||
| + | Jan Stühmer received his Diploma degree (with distinction) in Computer Science from Dresden University of Technology in 2010. In his diploma thesis he developed a novel method that allows dense 3D reconstructions with a handheld camera. In 2014/2015 he stayed as a research intern with [[http:// | ||
| + | |{{: | ||
| + | |||
| + | ---- | ||
| + | |||
| + | ====== Research Interests ====== | ||
| + | |||
| + | Geometry Processing, Segmentation, | ||
| + | |||
| + | |||
| + | I am working in the following research areas: | ||
| + | |||
| + | === Novel Methods for Time of Flight Based Tracking and Reconstruction === | ||
| + | < | ||
| + | |||
| + | We propose a novel tracking method that directly performs model based tracking on the raw infrared signal of a Time-Of-Flight camera which allows us to reconstruct the object’s depth at an order of magnitude higher frame-rate. | ||
| + | Even when the depth reconstruction fails due to fast motion of the object, our method can track the moving object. | ||
| + | |||
| + | \\ | ||
| + | \\ | ||
| + | |||
| + | === Topological Constraints in Image Segmentation === | ||
| + | {{: | ||
| + | |||
| + | Especially in biomedical image segmentation and denoising | ||
| + | the structures of interest show a thin and fine detailed shape. | ||
| + | While state-of-the-art segmentation methods | ||
| + | perform well for segmenting compact objects, | ||
| + | their performance on thin structures is often | ||
| + | not satisfying. The commonly used length regularizer suppresses small structures and the correct topology cannot be | ||
| + | reconstructed. | ||
| + | To overcome this limitation, we introduce a novel | ||
| + | algorithmic framework, that allows to preserve the connectivity | ||
| + | of the object. | ||
| + | We show that our method can be successfully applied to | ||
| + | medical image segmentation problems in angiography and | ||
| + | retinal blood vessel extraction, where thin structures otherwise would not be preserved by boundary length regularizers. | ||
| + | |||
| + | \\ | ||
| + | \\ | ||
| + | |||
| + | === Realtime 3D Reconstruction === | ||
| + | < | ||
| + | |||
| + | We present a novel variational approach to estimate dense | ||
| + | depth maps from multiple images in real-time using a hand-held camera. | ||
| + | Robust penalizers for both data term and regularizer allow to preserve discontinuities | ||
| + | in the depth map. We demonstrate that the integration of multiple images substantially increases the robustness of estimated depth maps to | ||
| + | noise in the input images. | ||
| + | |||
| + | \\ | ||
| + | \\ | ||
| + | |||
| + | === Image Segmentation, | ||
| + | {{: | ||
| + | |||
| + | In this project, we quantified the role of cell-cell adhesion for collective migration during embryogenesis of the developing zebrafish. | ||
| + | To allow an accurate quantification of cell migration in vivo, we developed a segmentation and tracking framework which is very robust to the salt and pepper noise observed in confocal laser microscope image data. | ||
| + | |||
| + | ---- | ||
| + | |||
| + | ====== Publications ====== | ||
| + | < | ||
| + | < | ||
| + | </ | ||
