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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Towards understanding what makes 3D objects appear simple or complex
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Sreenivas R. Sukumar, Imaging, Robotics and Intelligent Systems Lab, The University of Tennessee, Knoxville, USA
David L. Page, Imaging, Robotics and Intelligent Systems Lab, The University of Tennessee, Knoxville, USA
Andreas F. Koschan, Imaging, Robotics and Intelligent Systems Lab, The University of Tennessee, Knoxville, USA
Mongi A. Abidi, Imaging, Robotics and Intelligent Systems Lab, The University of Tennessee, Knoxville, USA
Humans perceive some objects more complex than others and learning or describing a particular object is directly related to the judged complexity. Towards the goal of understanding why the geometry of some 3D objects appear more complex than others, we conducted a psychophysical study and identified contributing attributes. Our experiments conclude that surface variation, symmetry, part count, simpler part decomposability, intricate details and topology are six significant dimensions that influence 3D visual shape complexity. With that knowledge, we present a method of quantifying complexity and show that the informational aspect of Shannon’s theory agrees with the human notion of shape complexity.
Citation:
Sreenivas R. Sukumar, David L. Page, Andreas F. Koschan, Mongi A. Abidi, "Towards understanding what makes 3D objects appear simple or complex," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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