2D observers for human 3D object recognition?

Zili Liu, Daniel Kersten

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Converging evidence has shown that human object recognition depends on familiarity with the images of an object. Further, the greater the similarity between objects, the stronger is the dependence on object appearance, and the more important two- dimensional (2D) image information becomes. These findings, however, do not rule out the use of 3D structural information in recognition, and the degree to which 3D information is used in visual memory is an important issue. Liu, Knill, & Kersten (1995) showed that any model that is restricted to rotations in the image plane of independent 2D templates could not account for human performance in discriminating novel object views. We now present results from models of generalized radial basis functions (GRBF), 2D nearest neighbor matching that allows 2D affine transformations, and a Bayesian statistical estimator that integrates over all possible 2D affine transformations. The performance of the human observers relative to each of the models is better for the novel views than for the familiar template views, suggesting that humans generalize better to novel views from template views. The Bayesian estimator yields the optimal performance with 2D affine transformations and independent 2D templates. Therefore, models of 2D affine matching operations with independent 2D templates are unlikely to account for human recognition performance.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262100762, 9780262100762
StatePublished - 1998
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: Dec 1 1997Dec 6 1997

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Country/TerritoryUnited States
CityDenver, CO

Bibliographical note

Funding Information:
DK was supported by a grant from the National Science Foundation, contract number SBR-9631682. We thank Ronen Basri, David Jacobs, David Knill, Michael Langer, Pascal Mamassian, Bosco Tjan, Daphna Weinshall, the anonymous reviewers and in particular, John Oliensis, for many helpful discussions. Weinshall pointed out to us the Werman–Weinshall theorem. Part of this work was presented at the Hong Kong International Workshop on ‘Theoretical Aspects of Neural Computation,’ Hong Kong University of Science and Technology, 1997; European Conference on Visual Perception (ECVP), Helsinki, Finland, 1997; ‘Neural Information Processing’ (NIPS), Denver, Colorado, 1997; and ‘International Conference on Computer Vision’ (ICCV), Mumbai, India, 1998.


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