Station-Keeping is an important capability of the Unmanned Underwater Vehicle in a variety of mission , including inspection and repair of undersea pipeline , and surveillance . Station-Keeping control includes two parts : motion estimation and Station-Keeping control system . In this thesis we propose a monocular vision system for determining the motion of an Unmanned Underwater Vehicle . The vehicle is equipped with a down-looking camera , which provides images of the sea-floor . The motion of vehicle is estimated with a feature-based mosaicking method which requires the extraction and the matching of relevant features . We designed a visual servo control system for maintaining the position of vehicle relative to a visual landmark , while maintaining a fixed depth .

<p>an Internal Navigational System and a Global Navigational Satellite System (GNSS). In navigational warfare the GNSS can be jammed, therefore are a third navigational system is needed. The system that has been tried in this thesis is camera based navigation. Through a video camera and a sensor reference the position is determined. This thesis will process the matching between the sensor reference and the video image.</p><p>Two methods have been implemented: normalized cross correlation and position determination through a homography. Normalized cross correlation creates a correlation matrix. The other method uses point correspondences between the images to determine a homography between the images. And through the homography obtain a position. The more point correspondences the better the position determination will be.</p><p>The results have been quite good. The methods have got the right position when the Euler angles of the UAV have been known. Normalized cross correlation has been the best method of the tested methods.</p>

The essential procedure of constructing 3-D building models in urban areas is to extract the building boundary footprint. In the past researches, the common procedures used in extracting the building footprint are applying edge detection, vectorization, and generalization. However, the derived boundary lines occasionally occur zigzag patterns, thus, it still needs further building footprint regularization. This study proposed a new approach in the point of view that the points, lines and polygons are the essential elements in reconstructing 3-D building models. The proposed new method is based on ¡§corner detection approach (CDA)¡¨ and ¡§Adjustment of building footprints and corner points (ABFCO)¡¨ algorithm on Light Detection And Ranging (LiDAR) or binary classification resultant imagery. This study implements Harris and Local Binary Pattern (LBP) corner detection, afterward, connects all detected points by using convex hull algorithm. However, ortho-non-rectangle buildings would compose poor outlines after convex hull. This study combines open and dilation morphology with the find ignored point algorithm to improve any incorrect connections. Finally, performs the ABFCO algorithm to those points which belong to the same boundary to generalize a line segment, and to figure out the intersections and boundary lines of the buildings. The experiment results have proved that the overall accuracy of LBP corner detection is about 3.5% higher than Harris corner detection, its overall accuracy is about 92% in rectangular buildings and about 91% in non-rectangular buildings, its standard deviation of boundary length is 0.29m and better than Harris¡¦s 0.55m. We also compared LBP corner detection with edge detection. The overall accuracy of corner detection is about 3% higher than edge detection, standard deviation of boundary length 0.37m is also better than edge detection 0.75m. This study not only proved the corner detection is better than edge detection from data, but also developed ABFCO algorithm is helpful for extracting more accurate building footprint lines.

ABSTRACT Optimizing Harris Corner Detection on GPGPUs Using CUDA The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.

an Internal Navigational System and a Global Navigational Satellite System (GNSS). In navigational warfare the GNSS can be jammed, therefore are a third navigational system is needed. The system that has been tried in this thesis is camera based navigation. Through a video camera and a sensor reference the position is determined. This thesis will process the matching between the sensor reference and the video image. Two methods have been implemented: normalized cross correlation and position determination through a homography. Normalized cross correlation creates a correlation matrix. The other method uses point correspondences between the images to determine a homography between the images. And through the homography obtain a position. The more point correspondences the better the position determination will be. The results have been quite good. The methods have got the right position when the Euler angles of the UAV have been known. Normalized cross correlation has been the best method of the tested methods.