Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [136]-142).

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 108-111).

No description available.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 74-76). Also available in electronic version. Access restricted to campus users.

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 94-99).

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [100]-108).

Inthisthesiswefocusontheimpactofilluminationchangesinvideoand we discuss how we can minimize the impact of illumination variance in video processing systems. Identifyingandremovingshadowsautomaticallyisaverywellestablished and an important topic in image and video processing. Having shadowless image data would benefit many other systems such as video surveillance, tracking and object recognition algorithms. Anovelapproachtoautomaticallydetectandremoveshadowsispresented in this paper. This new method is based on the observation that, owing to the relative movement of the sun, the length and position of a shadow changes linearly over a relatively long period of time in outdoor environments,wecanconvenientlydistinguishashadowfromotherdark regions in an input video. Then we can identify the Reference Shadow as the one with the highest confidence of the mentioned linear changes. Once one shadow is detected, the rest of the shadow can also be identifiedandremoved. Wehaveprovidedmanyexperimentsandourmethod is fully capable of detecting and removing the shadows of stationary and moving objects. Additionally we have explained how reference shadows can be used to detect textures that reflect the light and shiny materials such as metal, glass and water. ...

For several decades, pediatricians used to believe that neonates do not feel pain. The American Academy of Pediatrics (AAP) recognized neonates' sense of pain in 1987. Since then, there have been many studies reporting a strong association between repeated pain exposure (under-treatment) and alterations in brain structure and function. This association has led to the increased use of anesthetic medications. However, recent studies found that the excessive use of analgesic medications (over-treatment) can cause many side effects. The current standard for assessing neonatal pain is discontinuous and suffers from inter-observer variations, which can lead to over- or under-treatment. Therefore, it is critical to address the shortcomings of the current standard and develop continuous and less subjective pain assessment tools. This dissertation introduces an automatic and comprehensive neonatal pain assessment system. The presented system is different from the previous ones in three principal ways. First, it is specifically designed to assess pain of neonates using data captured while they are hospitalized in the Neonatal Intensive Care Units (NICU). Second, it dynamically analyzes neonatal pain as it unfolds in a particular pattern over time. Third, it combines visual, vocal, and physiological signals to create a system that continues to assess pain even when one or more signals become temporarily unavailable. The presented system has four main components. The first three components consist of novel algorithms for analyzing the visual, vocal, and physiological signals separately. The last component combines all the three signals to create a multimodal pain assessment system. The performance of the system in recognizing pain events is comparable to that of trained nurses; hence, it demonstrates the feasibility of automatic pain assessment in typical neonatal care environments.