At present, there does not exist an accepted quantitative method for inpainting evaluation. Psychophysical experiments show, however, that human opinion can be reliably predicted by computational models of human attention. Furthermore, adopting these psychophysical concepts in the design of inpainting algorithms can improve their output quality and efficiency. Thus, by emphasizing human observation of inpainted imagery rather than fitting purely geometric or physics-based models, it is possible to drastically improve the state-of-the-art in inpainting.
This material is based upon work supported by Eastman Kodak Gifts and NYSTAR Award #C040130. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the above named organizations.
|Advisor:||Brown, Christopher M., Singhal, Amit|
|School:||University of Rochester|
|School Location:||United States -- New York|
|Source:||DAI-B 71/03, Dissertation Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Computer vision, Image processing, Inpainting, Machine vision|
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