What Object Attributes Determine Canonical Views?

We investigated preferred or canonical views for familiar and three-dimensional nonsense objects using computer-graphics psychophysics. We assessed the canonical views for objects by allowing participants to actively rotate realistically shaded three-dimensional models in realtime. Objects were viewed on a Silicon Graphics workstation and manipulated in virtual space with a three-degree-of-freedom input device. In the first experiment, participants adjusted each object to the viewpoint from which they would take a photograph if they planned to use the object to illustrate a brochure. In the second experiment, participants mentally imaged each object on the basis of the name and then adjusted the object to the viewpoint from which they imagined it. In both experiments, there was a large degree of consistency across participants in terms of the preferred view for a given object. Our results provide new insights on the geometrical, experiential, and functional attributes that determine canonical views under ecological conditions.

[1]  J. Koenderink,et al.  The Shape of Smooth Objects and the Way Contours End , 1982, Perception.

[2]  B. Heller Circular Statistics in Biology, Edward Batschelet. Academic Press, London & New York (1981), 371, Price $69.50 , 1983 .

[3]  Jan J. Koenderink,et al.  An internal representation for solid shape based on the topological properties of the apparent contour , 1987 .

[4]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[5]  D I Perrett,et al.  Characteristic Views and the Visual Inspection of Simple Faceted and Smooth Objects: ‘Tetrahedra and Potatoes’ , 1988, Perception.

[6]  Geoffrey E. Hinton,et al.  Scene-based and viewer-centered representations for comparing shapes , 1988, Cognition.

[7]  M. Tarr,et al.  Mental rotation and orientation-dependence in shape recognition , 1989, Cognitive Psychology.

[8]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[9]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[10]  M. Harries,et al.  Preferential Inspection of Views of 3-D Model Heads , 1991, Perception.

[11]  S. Edelman,et al.  Orientation dependence in the recognition of familiar and novel views of three-dimensional objects , 1992, Vision Research.

[12]  M. Harries,et al.  Use of Preferential Inspection to Define the Viewing Sphere and Characteristic Views of an Arbitrary Machined Tool Part , 1992, Perception.

[13]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[14]  I. Biederman,et al.  Recognizing depth-rotated objects: evidence and conditions for three-dimensional viewpoint invariance. , 1993, Journal of experimental psychology. Human perception and performance.

[15]  S. Edelman,et al.  Canonical views in object representation and recognition , 1994, Vision Research.

[16]  David I. Perrett,et al.  Issues of representation in object vision , 1994 .

[17]  H H Bülthoff,et al.  How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.

[18]  M. Farah,et al.  The inverted face inversion effect in prosopagnosia: Evidence for mandatory, face-specific perceptual mechanisms , 1995, Vision Research.

[19]  K Verfaillie,et al.  A corpus of 714 full-color images of depth-rotated objects , 1995, Perception & psychophysics.

[20]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[21]  M. Tarr Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects , 1995, Psychonomic bulletin & review.

[22]  N. Logothetis,et al.  Psychophysical and physiological evidence for viewer-centered object representations in the primate. , 1995, Cerebral cortex.

[23]  H. Bülthoff,et al.  Face recognition under varying poses: The role of texture and shape , 1996, Vision Research.

[24]  Bernhard Schölkopf,et al.  Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models , 1996, ICANN.

[25]  Tomaso A. Poggio,et al.  3D Object Recognition: A Model of View-Tuned Neurons , 1996, NIPS.

[26]  K. Hoffmann,et al.  Visual Inspection of Three-Dimensional Objects by Human Observers , 1996, Perception.