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dc.contributor.authorAlthwaini, Abdulkareem Ali-
dc.description.abstractImage segmentation plays a vital role in applications such as remote sensing. For this example, remote sensing, aerial image segmentation is a special case of image segmentation. There are some unique features of aerial images, like noise in natural landscapes, which need to be addressed in order to obtain an optimal solution. Bushes and rocks are examples of landscape features with diverse and variable pixel values that need to be distinguished by the segmentation process. Smoothing filters present a common solution to address the problem of noise in images, as does aerial image segmentation. There are several image segmentation techniques used for aerial image segmentation. Some of these techniques are more sensitive to noise problems, and are necessary to discriminate between different smoothing filters. In this thesis, a number of different aspects of aerial image segmentation and their solutions are explained. In addition to this, a novel smoothing filter is introduced and compared with other methods using different segmentation techniques. Finally, all of the previous points are applied to a real world problem.en_CA
dc.subjectAerial image segmentationen_CA
dc.subjectsmoothing filtersen_CA
dc.subjectk-means classifyingen_CA
dc.subjectc-means fuzzy classifyingen_CA
dc.titleAerial image segmentationen_CA
dc.description.degreeMaster of Science (MSc) in Computational Sciencesen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

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