In range sensing with time-multiplexed structured light, there is a trade-off between accuracy, robustness and the acquisition period. The acquisition period is lower bounded by the product of the number of projection patterns and the time needed for acquiring a single image. In this paper a novel structured light method is described. Adaptation of the number and form of the projection patterns to the characteristics of the scene takes place as part of the acquisition process. Noise margins are matched to the actual noise level, thus reducing the number of projection patterns to the necessary minimum. Color is used for light plane labeling. The dimension of the pattern space (and the noise margins) are thus increased without raising the number of projection patterns. It is shown that the color of an impinging light plane can be identified from the image of the illuminated scene, even with colorful scenes. Identification is local and does not rely on spatial color sequences. Therefore, in comparison to other color structured light techniques, assumptions about smoothness and color neutrality of the scene can be relaxed. The suggested approach has been implemented and the theoretical results are supported by experiments.

A methodology for the optimal design of projection patterns for stereometric structured light systems is presented. It draws on the similarity as well as the difference between the design of projection patterns and the design of optimal signals for digital communication. Seemingly unrelated structured light methods, such as the Gray code scheme and intensity ratio techniques, are unified as special cases within the suggested theoretical framework. The design of K projection patterns for a structured light system with L distinct planes of light turns out to be equivalent to the placement of L points in a K dimensional space subject to certain constraints. Optimal design in the MSE sense can be defined, but leads to an intractable multi-parameter global optimization problem. Intuitively appealing suboptimal solutions are derived from the family of K dimensional space-filling Hilbert curves. The theoretical results are supported by experiments.

A CMOS 64 # 64 pixel area image sensor chip using Sigma-Delta modulation at each pixel for A#D conversion is described. The image data output is digital. The chip was fabricated using a 1.2#mtwo layer metal single layer poly n-well CMOS process. Each pixel block consists of a phototransistor and 22 MOS transistors. Test results demonstrate a dynamic range potentially greater than 93dB, a signal to noise ratio #SNR# of up to 61dB, and dissipation of less than 1mW with a 5V power supply. 1 Boyd Fowler, Abbas El Gamal, and David X. D. Yang 2 Charge-coupled devices #CCD# are at present the most widely used technology for implementing area image sensors. CCD image sensors have their shortcomings, however. They su#er from low yields, they consume too muchpower #3#, and they are plagued with SNR limitations due to the shifting and detection of analog charge packets, and the fact that data is communicated o# chip in analog form. Several alternatives to CCD area image sensors that use st...

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

This paper shows the power of the combination of smart sensor technique and real-time parallel processing. This implementation results in a very compact system which consists only of four cameras and an extension board to a PC. 1