The environment in which robotic fruit harv_e~ters work presents many challenges. Developers of a system that aims at performing in this environment need to take into account its inherent variability and devise their system to be robust enough to perform acceptably in all cases. This is no easy task, and a lot of work remains to be completed before the perfect fruit-harvesting robot is designed. This thesis attempts to take a step towards this goal by outlining an algorithm that would allow a vision-controlled robotic harvester to detect and accurately locate oranges in the canopy of an orange tree_ Most of the other researchers that have addressed this issue did not tackle the problem of accurately locating the oranges in an image of an orange tree, only differentiating the orange regions from the rest of the picture. In a previous work at UCF by Joakim Eriksson a system was developed that accurately locates the single, non occluded oranges in a color image of an orange tree. In this thesis, this initial work will be improved upon to give the system the capability of finding occluded oranges, and oranges inside a cluster increasing the overall detection accuracy at locating oranges. Accurately pinpointing the location of the orange would allow a robotic harvester to cut the stem of the orange by either scanning the top of the orange with a laser or by directing a robotic arm towards the stem to manually cut it. Future work needs to address the collection of the oranges after the robot has harvested them.

The problem of tracking moving objects in a video sequence is a well known and well researched problem in Computer Vision. Tracking moving objects is a basic tool which allows the development of solutions to complex problems such as target acquisition, automatic surveillance, action recognition, etc. Tracking problems and solutions generally deal with video that has relatively good frame-rates, i.e. from 15 to 30 frames per second, and the objects in motion do not exhibit huge jumps. However, if the video frame rate is low or, more precisely, the objects in motion move large distances from frame to frame, current tracking methods will perform very poorly This thesis proposes a method of tracking that will allow for large spatial discontinuities in object motion and still be able to track successfully. It demonstrates the feasibility of tracking in these sequences. Results are given from application of the proposed method to video sequences taken at 2 frames per second.

This thesis covers two main areas which are related to the mapping and examination of the ionosphere. The first examines the performance and specific nuances of various state-of-the-art interpolation methods with specific application to mapping the ionosphere. This work forms the most widely scoped examination of interpolation technique for ionospheric imaging to date, and includes the introduction of normalised convolution techniques to geophysical data. In this study, adaptive-normalised convolution was found to perform well in ionospheric electron content mapping, and the popular technique, kriging was found to have problems which limit its usefulness. The second, is the development and examination of automatic data-driven motion estimation methods for use on ionospheric electron content data. Particular emphasis is given to storm events, during which characteristic shapes appear and move across the North Pole. This is a particular challenge, as images covering this region tend to have a very-low resolution. Several motion estimation methods are developed and applied to such data, including methods based on optical flow, correlation and boundarycorrespondence. Correlation and relaxation labelling based methods are found to perform reasonably, and boundary based methods based on shape-context matching are found to perform well, when coupled with a regularisation stage. Overall, the techniques examined and developed here will help advance the process of examining the features and morphology of the ionosphere, both during storms and quiet times.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 172-182).

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 96-106).

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Techn. Univ., Habil.-Schr.--Hamburg, 1992.

Thesis (M.C.S.)--Carleton University, 2003. / Includes bibliographical references (p. 74-75). Also available in electronic format on the Internet.

Freie Univ., Diss.--Berlin, 2002. / References S. [209] - 220.

Thesis (M.S.)--University of North Texas, Aug., 2009. / Title from title page display. Includes bibliographical references.