Painterly rendered portraits from photographs using a knowledge-based approach

Portrait artists using oils, acrylics or pastels use a specific but open human vision methodology to create a painterly portrait
of a live sitter. When they must use a photograph as source, artists augment their process, since photographs have: different
focusing - everything is in focus or focused in vertical planes; value clumping - the camera darkens the shadows and lightens
the bright areas; as well as color and perspective distortion. In general, artistic methodology attempts the following: from the
photograph, the painting must 'simplify, compose and leave out what?s irrelevant, emphasizing what?s important'. While
seemingly a qualitative goal, artists use known techniques such as relying on source tone over color to indirect into a
semantic color temperature model, use brush and tonal "sharpness" to create a center of interest, lost and found edges to
move the viewers gaze through the image towards the center of interest as well as other techniques to filter and emphasize.
Our work attempts to create a knowledge domain of the portrait painter process and incorporate this knowledge into a multispace
parameterized system that can create an array of NPR painterly rendering output by analyzing the photographic-based
input which informs the semantic knowledge rules.