This work presents an image segmentation method for range data that uses multi-scale wavelet analysis in combination with statistical pattern recognition. We train a pattern-recognition system with scale-space data from the edge points of a training image. Once trained the system can determine the degree of edgehess of points in a new image. Before designing the segmentation system we set forth several goals. We desire that the system detect boundaries of small as well as large objects, be robust, and have few or no free parameters.

In this paper, we investigate the impact of intensity edge maps (IEMs) on the segmentation of noisy range images. Two edgebased segmentation algorithms are considered. The first is a watershed-based segmentation technique and the other is the scan-line grouping technique. Each of these algorithms is implemented in two different forms. In the first form, an IEM is fused with the range edge map prior to segmentation. In the second form, the range edge map alone is used. The performance of each algorithm, with and without the use of the IEM information, is evaluated and reported in terms of correct segmentation rate. For our experiments, two sets of real range images are used. The first set comprises inherently noisy images. The other set is composed of images with varying levels of artificial, additive Gaussian noise. The experimental results indicate that the use of IEMs can significantly improve edge-based segmentation of noisy range images. Considering these results, it seems that segmentation tasks involving range images captured by noisy scanners would benefit from the use of IEM information. Additionally, the experiments indicate that higher quality edge information can be obtained by fusing range and intensity edge information.

I would like to thank the people who have helped and supported me in completing this work. First of all I would like to thank my supervisor Dr. Mongi Abidi for his support, patience, and guidance during these years of my study at UTK. Also, I would like to thank the other members of my dissertation committee, Dr. Collins, Dr. Koch, Dr. Qi, and Dr. Roberts, for their interests in this work and their insightful advice to this dissertation. I am very grateful to Dr. Koschan and Dr. Paik for their invaluable suggestions to my research and this dissertation. Special thanks to Dr. David Page for our inspiring conversations and his many helpful comments on this dissertation. I would also like to thank the faculty, staff and students in the IRIS laboratory who created an excellent environment where I have enjoyed working. I am indebted to Vicki Courtney-Smith for her helping with my various administrative needs, to Mark Mitckes for his proofreading of my outgoing documents, and to the fellow students including Brad Grinstead, Umayal Chidambaram, Tak Motoyama, Justin Acuff and many others for their kind help. Last but not least, I want to express gratitude to my family. Thanks to my parents Mingyu Zhang and Shuiyue Xu for their consistent support during these academic years. Huge thanks