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We present an automatic, real-time video and image abstraction framework
that abstracts imagery by modifying the contrast of visually important
features, namely luminance and color opponency. We reduce contrast in
low-contrast regions using an approximation to anisotropic diffusion,
and artificially increase contrast in higher contrast regions with
difference-of-Gaussian edges. The abstraction step is extensible and
allows for artistic or data-driven control. Abstracted images can
optionally be stylized using soft color quantization to create
cartoon-like effects with good temporal coherence. Our framework design
is highly parallel, allowing for a GPU-based, real-time implementation.
We evaluate the effectiveness of our abstraction framework with a
user-study and find that participants are faster at naming abstracted
faces of known persons compared to photographs. Participants are also
better at remembering abstracted images of arbitrary scenes in a memory
task. |
