A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

Vision-based control of near-obstacle flight

Lightweight micro unmanned aerial vehicles (micro-UAVs) capable of autonomous flight in natural and urban environments have a large potential for civil and commercial applications, including environmental monitoring, forest fire monitoring, homeland security, traffic monitoring, aerial imagery, mapping and search and rescue. Smaller micro-UAVs capable of flying inside houses or small indoor environments have further applications in the domain of surveillance, search and rescue and entertainment. These applications require the capability to fly near to the ground and amongst obstacles. Existing UAVs rely on GPS and AHRS (attitude heading reference system) to control their flight and are unable to detect and avoid obstacles. Active distance sensors such as radars or laser range finders could be used to measure distances to obstacles, but are typically too heavy and power-consuming to be embedded on lightweight systems. In this thesis, we draw inspiration from biology and explore alternative approaches to flight control that allow aircraft to fly near obstacles. We show that optic flow can be used on flying platforms to estimate the proximity of obstacles and propose a novel control strategy, called optiPilot, for vision-based near-obstacle flight. Thanks to optiPilot, we demonstrate for the first time autonomous near-obstacle flight of micro-UAVs, both indoor and outdoor, without relying on an AHRS nor external beacons such as GPS. The control strategy only requires a small series of optic flow sensors, two rate gyroscopes and an airspeed sensor. It can run on a tiny embedded microcontroller in realtime. Despite its simplicity, optiPilot is able to fully control the aircraft, including altitude regulation, attitude stabilisation, obstacle avoidance, landing and take-off. This parsimony, inherited from the biology of flying insects, contrasts with the complexity of the systems used so far for flight control while offering more capabilities. The results presented in this thesis contribute to a better understanding of the minimal requirements, in terms of sensing and control architecture, that enable animals and artificial systems to fly and bring closer to reality the perspective of using lightweight and inexpensive micro-UAV for civilian purposes

Flow Imaging Using MRI: Quantification and Analysis

A complex and challenging problem in flow study is to obtain quantitative flow information in opaque systems, for example, blood flow in biological systems and flow channels in chemical reactors. In this regard, MRI is superior to the conventional optical flow imaging or ultrasonic Doppler imaging. However, for high speed flows, complex flow behaviors and turbulences make it difficult to image and analyze the flows. In MR flow imaging, MR tagging technique has demonstrated its ability to simultaneously visualize motion in a sequence of images. Moreover, a quantification method, namely HARmonic Phase (HARP) analysis, can extract a dense velocity field from tagged MR image sequence with minimal manual intervention. In this work, we developed and validated two new MRI methods for quantification of very rapid flows. First, HARP was integrated with a fast MRI imaging method called SEA (Single Echo Acquisition) to image and analyze high velocity flows. Second, an improved HARP method was developed to deal with tag fading and data noise in the raw MRI data. Specifically, a regularization method that incorporates the law of flow dynamics in the HARP analysis was developed. Finally, the methods were validated using results from the computational fluid dynamics (CFD) and the conventional optimal flow imaging based on particle image velocimetry (PIV). The results demonstrated the improvement from the quantification using solely the conventional HARP method