3 research outputs found

    Advances in Motion Estimators for Applications in Computer Vision

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    abstract: Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained. The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies. In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data. In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets. In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

    Detection of faults in subsea crude oil pipelines by machine learning assisted process monitoring

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    Crude oil pipelines are a major infrastructure investment for subsea oilfields and for platform-to-shore transportation and continue to be the preferred method of transporting crude oil over short to medium distances due to low operating costs and high safety record compared to other transport methods. Their remote and often extreme locations limit their access and increases risks of human and environmental hazards in the event of failure. Due to their frequent placement in deep water and hostile environments, inspection and identification of faults (such as leaks and flow restrictions) are difficult, expensive, and hazardous. Faults are often only identified in accidents or upon routine inspection and after significant material losses and/or environmental damage has occurred. The oil industry would benefit from a low-cost and timely method of fault detection. This thesis proposes such a method by augmenting process monitoring with Machine Learning (ML). This thesis investigates the relationship between pressure change, velocity change, and temperature of crude oil through a pipeline. A representative dataset of crude oil flow is generated by computational fluid dynamics (CFD) and used to train a ML algorithm to develop a model of fluid behavior under normal pipeline operations over a range of typical flow rates and temperatures. CFD data are then collected under several simulated fault conditions: leaks of 10 and 20% of the inner cross-sectional area of the pipe, and a restriction to flow of 50% of the cross-sectional area. This thesis demonstrates that the ML algorithm can be trained to model the system under normal conditions, thereby successfully recognizing a fault condition as non-conforming and indicative of a statistically significant change in pipeline operation. It is further able to identify the fault type based on the pattern observed in the new data. This work demonstrates that ML may be a low-risk, low-cost, and accurate method of monitoring a subsea crude oil pipeline for optimal performance and fault detection without the need to introduce special equipment to a subsea pipeline network, assuming flowmeters and temperature probes are employed for process monitoring. This thesis develops the model algorithm, and it is hoped that the results of this study provide a basis for the integration of machine learning and further “big data” techniques in loss prevention and health and environmental protection in the offshore oil industry and elsewhere

    Improving the Accuracy of Two-Color Multiview (2CMV) Advanced Geospatial Information (AGI) Products Using Unsupervised Feature Learning and Optical Flow

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    In two-color multiview (2CMV) advanced geospatial information (AGI) products, temporal changes in synthetic aperture radar (SAR) images acquired at different times are detected, colorized, and overlaid on an initial image such that new features are represented in cyan, and features that have disappeared are represented in red. Accurate detection of temporal changes in 2CMV AGI products can be challenging because of ’speckle noise’ susceptibility and false positives that result from small orientation differences between objects imaged at different times. Accordingly, 2CMV products are often dominated by colored pixels when changes are detected via simple pixel-wise cross-correlation. The state-of-the-art in SAR image processing demonstrates that generating efficient 2CMV products, while accounting for the aforementioned problem cases, has not been well addressed. We propose a methodology to address the aforementioned two problem cases. Before detecting temporal changes, speckle and smoothing filters mitigate the effects of speckle noise. To detect temporal changes, we propose using unsupervised feature learning algorithms in conjunction with optical flow algorithms that track the motion of objects across time in small regions of interest. The proposed framework for distinguishing between actual motion and misregistration can lead to more accurate and meaningful change detection and improve object extraction from an SAR AGI product
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