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All Projects
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The research focuses on investigating the pupillary light reflex to reveal the nervous system changes in rats with schizophrenia-like alterations. |
Automatic calibration of ominidirectional, perspective camera and lidar images is important for 3D model construction, robot navigation and environment mapping. The proposed methodology is without any specific constraint on source of data and the calibration can be performed using a single pair of lidar-camera image without any implicit correspondence between image points.
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Chlamydiae are obligate intracellular bacteria that propagate in the inclusion, a specific niche inside the host cell. We developed an automatic inclusion counting system based on a commercially available DNA chip scanner. Fluorescently labeled inclusions are detected by the scanner, and the image is processed by ChlamyCount, a custom plugin of the ImageJ software environment.
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The goal of our project is to develop new algorithms that exploit the imaging and connectivity capabilities of modern mobil devices (smartphones and tablets) and provide new visual information contents for the users.
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We are researching and developing image processing algorithms for dermatological applications in diagnostic and decision making systems as well as for education. This includes the creation of a personalized surface model from a set of color and depth camera images, detection and classification of dermatological findings, such as psoriasis lesions and plaques, as well as longitudinal analysis of changes.
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A novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework.
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The aim of this research is to study image features and processing methods for detection of visual code regions (1D barcode, 2D datamatrix, OCR characters), with special focus on highly accurate and efficient algorithms that can be adapted for real industrial applications. |
Correspondance-less alignment of 3D non-rigid objects using parametric nonlinear deformation models. Our algorithms are able to estimate the parameters of the deformation without an established set of correspondences, thus providing a fully automatic and computationally efficient registration framework for 3D objects.
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The aim of this project is to improve the well-known methods of continuous and discrete tomography. We study image processing methods that can be applied directly on the projections to improve reconstruction quality. We improve techniques to measure the quality of the reconstructed images, and develop image reconstruction methods based on parallel processing using GPUs. We also investigate which are the most valuable projections, if the reconstruction is performed from just a few of them.
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We apply and evaluate machine learning methods to obtain information purely from the tomographic projections of an image. We design and implement discrete tomographic reconstruction methods that can exploit the knowledge obtained in this way.
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