Decoupled Multicamera Sensing for Flexible View Generation

10.1155/2016/8137859Journal of Sensors2016813785

Weighted and filtered mutual information: A Metric for the automated creation of panoramas from views of complex scenes

To contribute a novel approach in the field of image registration and panorama creation, this algorithm foregoes any scene knowledge, requiring only modest scene overlap and an acceptable amount of entropy within each overlapping view. The weighted and filtered mutual information (WFMI) algorithm has been developed for multiple stationary, color, surveillance video camera views and relies on color gradients for feature correspondence. This is a novel extension of well-established maximization of mutual information (MMI) algorithms. Where MMI algorithms are typically applied to high altitude photography and medical imaging (scenes with relatively simple shapes and affine relationships between views), the WFMI algorithm has been designed for scenes with occluded objects and significant parallax variation between non-affine related views. Despite these typically non-affine surveillance scenarios, searching in the affine space for a homography is a practical assumption that provides computational efficiency and accurate results, even with complex scene views. The WFMI algorithm can perfectly register affine views, performs exceptionally well with near-affine related views, and in complex scene views (well beyond affine constraints) the WFMI algorithm provides an accurate estimate of the overlap regions between the views. The WFMI algorithm uses simple calculations (vector field color gradient, Laplacian filtering, and feature histograms) to generate the WFMI metric and provide the optimal affine relationship. This algorithm is unique when compared to typical MMI algorithms and modern registration algorithms because it avoids almost all a priori knowledge and calculations, while still providing an accurate or useful estimate for realistic scenes. With mutual information weighting and the Laplacian filtering operation, the WFMI algorithm overcomes the failures of typical MMI algorithms in scenes where complex or occluded shapes do not provide sufficiently large peaks in the mutual information maps to determine the overlap region. This work has currently been applied to individual video frames and it will be shown that future work could easily extend the algorithm into utilizing motion information or temporal frame registrations to enhance scenes with smaller overlap regions, lower entropy, or even more significant parallax and occlusion variations between views

Damage detection and monitoring for tunnel inspection based on computer vision

The deterioration of the underground infrastructure of the major cities around the world, due to ageing, has become a topic of great concern among engineers. Visual inspection, as part of the routine maintenance procedures, is a common practice used in the condition assessment of infrastructure to ensure its safety and serviceability. This practice, however, is labour-intensive, costly and inaccurate and, therefore, a new system based on computer vision technology is presented in this thesis, aiming to tackle these inadequacies. This thesis proposes a novel mosaicing system for inspection reporting, which can create an almost distortion-free mosaic of tunnels, thus allowing a large area of tunnels to be visualised. The system relies on Structure from Motion (SFM), which enables the system to cope with images with a general camera motion, in contrast to standard mosaicing software that can cope only with a strict camera motion. The system involves the automatic robust estimation of a 3D cylindrical surface using a Support Vector Machine to classify 3D points to improve the accuracy of the estimation. It is shown that some curvatures are observed in the mosaics when an inaccurate surface is used for mosaicing, while the mosaics from a surface estimated using the proposed method are almost distortion-free. New feature matching algorithms aiming to improve the performance of SFM systems are proposed. These algorithms apply a spatial consistency constraint to match features with a similar topography, in contrast to other matching algorithms that rely on matching based on the similar appearance of local image patches. The Shape Context and Random Forest algorithms are combined in the proposed algorithm, revealing promising results. The final contribution is a new change detection system for monitoring cracks in multi-temporal images. The system can cope with images with a general camera motion achieved by geometrical registration using SFM, unlike other systems that assume fixed or controlled cameras. The system performs photometric normalisation to cope with illumination variation in the images, and also a motion-invariant change detection algorithm is applied to handle deformable objects. It is shown that the results from the proposed change detection system are still impractical for use with tunnel images from a real environment, and further study is required

Forschungsbericht Universität Mannheim 2008 / 2009

Die Universität Mannheim hat seit ihrer Entstehung ein spezifisches Forschungsprofil, welches sich in ihrer Entwicklung und derz eitigen Struktur deutlich widerspiegelt. Es ist geprägt von national und international sehr anerkannten Wirtschafts- und Sozialwissenschaften und deren Vernetzung mit leistungsstarken Geisteswissenschaften, Rechtswissenschaft sowie Mathematik und Informatik. Die Universität Mannheim wird auch in Zukunft einerseits die Forschungsschwerpunkte in den Wirtschafts- und Sozialwissenschaften fördern und andererseits eine interdisziplinäre Kultur im Zusammenspiel aller Fächer der Universität anstreben