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 .

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.

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As the popularity of the digital Web-cam¡Athese devices are more and more cheaper and powerful. We can apply computer vision techniques with camera and projector to build a more convenient presentation system. In presentation, sometimes due to the position of projector, the images appear the perspective distortion (keystone distortion). The user should manually adjust the position of projector or use the keystone corrections of the projector. But when the distortion is not trapezium, the built-in keystone corrections are not suitable in this situation. We present a computer-vision based method that uses a Web-cam to calibrate the keystone distortion. The Web-cam takes the images that the projector projected on the wall. If the Web-cam observes keystone distortions of the projected images, we use a geometric transform that pre-warps the images in the projector frame, such that these images appears rectangle with known aspect ratio after being projected on the wall. Besides, we implement the virtual buttons that allow users to interact with the computer. The virtual buttons means that when the camera detect the laser point is on the virtual buttons, computer triggers the event as the virtual button being pushed. This paper uses point-matching pairs to obtain the homography between camera image frame and source image frame. The homography, that is the fundamental of calibrating perspective distortions also help us to search the position of the laser point.

In this thesis, we have evaluated the memory characteristics and parallel behaviour of the SUSAN (Smallest Univalue Segment Assimilating Nucleus) and Harris corner detection algorithms. Our purpose is understanding how the memory affects the predictability of these algorithms and furthermore how we can use multi-core machines to improve the execution time of such algorithms. By investigating the execution pattern of the SUSAN and Harris corner detection algorithms, we were able of breaking down the algorithms into parallelizable parts and non-parallelizable parts. We implemented a fork-join model on the parallelizable parts of these two algorithms and we were able to achieve a 7.9--8 times speedup on the two corner detection algorithms using an 8-core P4080 machine. For the sake of a wider study, we also executed these parallel adaptations on 4 different Intel platforms which generated similar results. The parallelized algorithms are also subjects for further improvement. We therefore investigated the memory characteristics of L1 data and instruction cache misses, cycles waiting for L2 cache miss loads, and TLB store misses. In these measurements, we found a strong correlation between L1 data cache replacement and the execution time. To encounter this memory issue, we implemented loop tiling techniques which were adjusted according to the L1 cache size of our test systems. Our tests of the tiling techniques exhibit a less fluctuating memory behaviour, which however comes at the cost of an increase in the execution time.

The purpose of this thesis is to study and investigate a practical and efficient implementation of corner orientation detection using multisteerable filters. First, practical theory involved in applying multisteerable filters for corner orientation estimation is presented. Methods to improve the efficiency with which multisteerable corner filters are applied to images are investigated and presented. Prior research in this area presented an optimization equation for determining the best match of corner orientations in images; however, little research has been done on optimization techniques to solve this equation. Optimization techniques to find the maximum response of a similarity function to determine how similar a corner feature is to a multioriented corner template are also explored and compared in this research.

<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>

<p>Feature extraction from a tensor based local image representation introduced by Knutsson in [37] is discussed. The tensor representation keeps statements of structure, certainty of statement and energy separate. Further processing for obtaining new features also having these three entities separate is achieved by the use of a new concept, tensor field filtering. Tensor filters for smoothing and for extraction of circular symmetries are presented and discussed in particular. These methods are used for corner detection and extraction of more global features such as lines in images. A novel method for grouping local orientation estimates into global line parameters is introduced. The method is based on a new parameter space, the Möbius Strip parameter space, which has similarities to the Hough transform. A local centroid clustering algorithm is used for classification in this space. The procedure automatically divides curves into line segments with appropriate lengths depending on the curvature. A linked list structure is built up for storing data in an efficient way.</p> / Ogiltigt nummer / annan version: I publ. nr 290:s ISBN: 91-7870-815-X.

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.

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