Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2009. / Includes bibliographical references (p. 47-49).

Thesis (M.S. in Joint Campaign Planning and Strategy)--Joint Forces Staff College, Joint Advanced Warfighting School, 2008. / Title from PDF title page; viewed on Oct. 17, 2008. "11 May 2008." Electronic version of original print document. Includes bibliographical references (p. 82-86).

There has been great interest in the use of small robotic helicopter vehicles over the last few years. Although there are regulatory issues involved in flying these that are still to be solved, they have the potential to provide a practical mobile aerial platform for a small fraction of the cost of a conventional manned helicopter. One potential class of applications for these is in searching for people, and this thesis explores a new generation of cameras which are suitable for this purpose. We propose HeatTrack, a novel algorithm to detect and track people in aerial imagery taken from a combined infrared/visible camera rig. A Local Binary Patterns (LBP) detector finds silhouettes in the infrared image which are used guide the search in the visible light image, and a Kalman filter combines information from both modalities in order to track a person more accurately than if only a single modality were available. We introduce a method for matching the thermal signature of a person to their corresponding patch in the visible modality, and show that this is more accurate than traditional homography-based matching. Furthermore, we propose a method for cancelling out camera motion which allows us to estimate a velocity for the person, and this helps in determining the location of a person in subsequent frames. HeatTrack demonstrates several advantages over tracking in the visible domain only, particularly in cases where the person shows up clearly in infrared. By narrowing down the search to the warmer parts of a scene, the detection of a person is faster than if the whole image were searched. The use of two imaging modalities instead of one makes the system more robust to occlusion; this, in combination with estimation of the velocity of a person, enables tracking even when information is lacking in either modality. To the best of our knowledge, this is the first published algorithm for tracking people in aerial imagery using a combined infrared/visible camera setup.

Thesis (M.S.)--Humboldt State University, 2006. / Includes bibliographical references (leaves 43-46). Also available via Humboldt Digital Scholar.

Multispectral aerial videography was used to reproduce the jurisdictional delineation of wetland area of approximately 50 hectares in Davis County, Utah Imagery from the system consisted of three-band composite with wavelengths covering 550 nm (±10 nm), 650 nm (±10 nm), and 850 nm (±10 nm). The site was overflown at three different flight dates during the 1992 growing season (June 2, July 22, October 1). Imagery resolution varied from 0.56 m to 0.81 m. Mosaiced images were analyzed with a Supervised clustering/maximum likelihood classifier, ISODATA clustering/Euclidan classifier, statistical clustering/maximum likelihood classifier, and fuzzy c-means clustering. Overall accuracies for wetland/upland designations as compared to ground truth data varied from 60% to 75%. The ISODATA method was the poorest performer for all dates and both of two accuracy testing techniques. Supervised clustering and statistical clustering were comparable with a slight edge in accuracy to the supervised clustering. The best all-round performer was the fuzzy c-means algorithm in terms of time spent and accuracy.

Thèse(M.Ress.Renouv.)-- Université du Québec à Chicoutimi, 2002. / Document électronique également accessible en format PDF. CaQCU

Thesis (Ph.D.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves x-xiv).

Thesis (M.S. in Joint Campaign Planning and Strategy)--Joint Forces Staff College, Joint Advanced Warfighting School, 2007. / Title from title screen; viewed on July 9, 2007. "April 2007." Electronic version of original print document. Includes bibliographical references (p. 61-66).

There is currently no effective tool available to quickly and economically measure a change in landmass in the setting of biomedical professionals and environmental specialists. The purpose of this study is to structure and demonstrate a statistical change-detection method using remotely sensed data that can detect the presence of an infectious land borne disease. Data sources included the Texas Department of Health database, which provided the types of infectious land borne diseases and indicated the geographical area to study. Methods of data collection included the gathering of images produced by digital orthophoto quadrangle and aerial videography and Landsat. Also, a method was developed to identify statistically the severity of changes of the landmass over a three-year period. Data analysis included using a unique statistical detection procedure to measure the severity of change in landmass when a disease was not present and when the disease was present. The statistical detection method was applied to two different remotely sensed platform types and again to two like remotely sensed platform types. The results indicated that when the statistical change detection method was used for two different types of remote sensing mediums (i.e.-digital orthophoto quadrangle and aerial videography), the results were negative due to skewed and unreliable data. However, when two like remote sensing mediums were used (i.e.- videography to videography and Landsat to Landsat) the results were positive and the data were reliable.

Thèse de mémoire (M.Ress.Renouv.)--Université du Québec à Chicoutimi, 1997. / Document électronique également accessible en format PDF. CaQCU