An Outdoor Stereo Camera System for the Generation of Real-World Benchmark Datasets with Ground Truth

In this report we describe a high-performance stereo camera system to capture image sequences with high temporal and spatial resolution for the evaluation of various image processing tasks. The system was primarily designed for complex outdoor and traffic scenes which frequently occur in the automotive industry, but is also suited for other applications. For this task the system is equipped with a very accurate inertial measurement unit and global positioning system, which provides exact camera movement and position data. The system is already in active use and has produced several terabyte of challenging image sequences which are available for download

Modular Optical Flow Estimation With Applications To Fluid Dynamics

Optical flow is the apparent motion of intensities in an image sequence. Its estimation has been studied for almost three decades. The results can be used in a wealth of possible applications ranging from scientific applications like experimental fluid dynamics over medical imaging to mobile computer games. The development of a single solution for all optical flow problems seems to be a worthwhile goal. However, in this thesis, we argue that this goal is unlikely to be achieved. We thoroughly motivate this hypothesis with theoretical and practical considerations. Based on the results, we identify two major problems that significantly complicate the research and development of new optical flow algorithms: First, very few reference implementations are publicly available. Second, not all relevant properties of the proposed algorithms are described in literature. In the first part of this thesis, our contribution is to alleviate both problems. First, we discuss a number of algorithm properties which should be known by the user. Second, by decomposing existing optical flow methods into their individual algorithm building blocks, shortly called modules, we propose to individually analyze the properties of each module independently. A large number of existing techniques is composed of relatively few existing modules. By implementing these modules in a software library called Charon and adding tools for the evaluation of the results, we contribute to the accessibility of reference implementations and to the possibility of analyzing algorithms by experiments. In the second part of this thesis, we contribute two modules which are vital for the estimation of fluid flows. They are specifically tuned to the imagery obtained for particle tracking velocimetry (PTV). We call the first module estimatibility measure. It detects those particle locations where fluid motion can be estimated. It is based on the constant position of the center of gravity of the connected components generated by a large number of thresholded versions of the original image. The module only needs a few intuitive parameters. Experiments indicate its robustness with respect to noise with varying mean and variance. To analyze the properties of this module we also provide a framework for simulating the particle image generation. The second module is a motion model based on unsupervised learning via principal component analysis. Training data is provided through Computational Fluid Dynamic (CFD) simulations. The model describes local ensembles of trajectories which can be fitted to the image sequence by means of a similarity measure. Together with a standard similarity measure and a simple optimization scheme we derive a new PTV method. Compared to existing techniques, we obtained superior results with respect to accuracy on real and synthetic sequences with known ground truth. All source code developed during the thesis is available as Open Source following the GNU Lesser General Public License (LGPL)

Optical Flow Estimation versus Motion Estimation

Optical flow estimation is often understood to be identical to dense image based motion estimation. However, only under certain assumptions does optical flow coincide with the projection of the actual 3D motion to the image plane. Most prominently, transparent and glossy scene-surfaces or changes in illumination introduce a difference between the motion of objects in the world and the apparent motion. In this paper we summarize the types of problems occuring in this field and show examples for illustration

Real-Time Imaging Reveals the Dynamics of Leukocyte Behaviour during Experimental Cerebral Malaria Pathogenesis

During experimental cerebral malaria (ECM) mice develop a lethal neuropathological syndrome associated with microcirculatory dysfunction and intravascular leukocyte sequestration. The precise spatio-temporal context in which the intravascular immune response unfolds is incompletely understood. We developed a 2-photon intravital microscopy (2P-IVM)-based brain-imaging model to monitor the real-time behaviour of leukocytes directly within the brain vasculature during ECM. Ly6Chi monocytes, but not neutrophils, started to accumulate in the blood vessels of Plasmodium berghei ANKA (PbA)-infected MacGreen mice, in which myeloid cells express GFP, one to two days prior to the onset of the neurological signs (NS). A decrease in the rolling speed of monocytes, a measure of endothelial cell activation, was associated with progressive worsening of clinical symptoms. Adoptive transfer experiments with defined immune cell subsets in recombinase activating gene (RAG)-1-deficient mice showed that these changes were mediated by Plasmodium-specific CD8+ T lymphocytes. A critical number of CD8+ T effectors was required to induce disease and monocyte adherence to the vasculature. Depletion of monocytes at the onset of disease symptoms resulted in decreased lymphocyte accumulation, suggesting reciprocal effects of monocytes and T cells on their recruitment within the brain. Together, our studies define the real-time kinetics of leukocyte behaviour in the central nervous system during ECM, and reveal a significant role for Plasmodium-specific CD8+ T lymphocytes in regulating vascular pathology in this disease. © 2014 Pai et al

C.: An adaptive confidence measure for optical flows based on linear subspace projections

Abstract. Confidence measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.