Computer Studies Bldg. Room 703
"Image inpainting" refers to the completion of holes in digital
imagery, regardless of whether the end result is object removal,
denoising, texture synthesis, etc.. The result of inpainting can
significantly diverge from the original image; in fact, this is
frequently the desired effect. As a result, fidelity-based techniques
for determining image quality do not apply. In point of fact, there
does not currently exist any accepted quantitative method for
inpainting evaluation. Psychophysical experiments show, however, that
human opinion of inpainted imagery 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 this talk, I will present some of the research that I have
performed regarding inpainting, its applications, and the use of
attention models. No prior background in image processing or
artificial intelligence is assumed, and I will present conceptual
information on inpainting and its applications before delving into my
contributions to the field.