BibTex Entry:

  author = {Holger Winnem{\"o}ller},
  year = 2006,
  title = {{Perceptually-motivated Non-Photorealistic Graphics}},
  address = {Evanston, Illinois, U.S.A.},
  school = {Northwestern University},
  abstract = {At a high level, computer graphics deals with conveying
             information to an observer by visual means. Generating realistic
             images for this task requires considerable time and computing
             resources. Human vision faces the opposite challenge: to distill
             knowledge of the world from a massive influx of visual
             information. It is reasonable to assume that synthetic images
             based on human perception and tailored for a given task can (1)
             decrease image synthesis costs by obviating a physically
             realistic lighting simulation, and (2) increase human task
             performance by omitting superfluous detail and enhancing visually
             important features. This dissertation argues that the connection
             between non-realistic depiction and human perception is a
             valuable tool to improve the effectiveness of computer-generated
             images to support visual communication tasks, and conversely, to
             learn more about human perception of such images. Artists have
             capitalized on non-realistic imagery to great effect, and have
             become masters of conveying complex and even abstract messages by
             visual means. The relatively new field of non-photorealistic
             computer graphics attempts to harness artistsí implicit expertise
             by imitating their visual styles, media, and tools, but only few
             works move beyond such simulations to verify the effectiveness of
             generated images with perceptual studies, or to investigate which
             stylistic elements are effective for a given visual communication
             task. This dissertation demonstrates the mutual beneficence of
             non-realistic computer graphics and perception with two rendering
             frameworks and accompanying psychophysical studies: (1) Inspired
             by low-level human perception, a novel image-based abstraction
             framework simplifies and enhances images to make them easier to
             understand and remember. (2) A non-realistic rendering framework
             generates isolated visual shape cues to study human perception of
             fast-moving objects. The first framework leverages perception to
             increase effectiveness of (non-realistic) images for
             visually-driven tasks, while the second framework uses
             non-realistic images to learn about task-specific perception,
             thus closing the loop. As instances of the bi-directional
             connections between perception and non-realistic imagery, the
             frameworks illustrate numerous benefits including effectiveness
             (e.g. better recognition of abstractions versus photographs),
             high performance (e.g. real-time image abstraction), and
             relevance (e.g. shape perception in non-impoverished conditions).}