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Abstract:
Converging evidence has shown that human object recognition
depends on observers' familiarity with objects' appearance. The
more similar the objects are, the stronger this dependence will be,
and the more important two-dimensional (2D) image information will
be. The degree to which 3D structural information is used, however,
remains an area of strong debate. Previously, we showed that all
models that allow 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
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, no models of 2D affine operations with
independent 2D templates account for the human performance.
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