Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

August 15, 1999, Vol. 11, No. 6, Pages 1331-1348
(doi: 10.1162/089976699300016269)
© 1999 Massachusetts Institute of Technology
Inferring Perceptual Saliency Fields from Viewpoint-Dependent Recognition Data
Article PDF (373.15 KB)
Abstract

We present an algorithm for computing the relative perceptual saliencies of the features of a three-dimensional object using either goodness-of-view scores measured at several viewpoints or perceptual similarities among several object views. This technique addresses the inverse, illposed version of the direct problem of predicting goodness-of-view scores or view point similarities when the object features are known. On the basis of a linear model for the direct problem, we solve the inverse problem using the method of regularization. The critical assumption we make to regularize the solution is that perceptual salience varies slowly on the surface of the object. The salient regions derived using this assumption empirically indicate what object structures are important in human three-dimensional object perception, a domain where theories typically have been based on somewhat ad hoc features.