Visual pattern recognition in humans |
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| Authors: | T. Caelli I. Rentschler W. Scheidler |
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| Affiliation: | (1) Institut für Medizinische Psychologie der Universität, Goethestrasse 31/1, D-8000 München 2, Federal Republic of Germany;(2) Present address: Alberta Centre for Machine Intelligence and Robotics, Department of Psychology, The University of Alberta, T6G 2E9 Edmonton, AB, Canada |
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| Abstract: | We have investigated how observers learn to classify compound Gabor signals as a function of their differentiating frequency components. Performance appears to be consistent with decision processes based upon the least squares minimum distance classifier (LSMDC) operating over a cartesian feature space consisting of the real (even) and imaginary (odd) components of the signals. The LSMDC model assumes observers form prototype signals, or adaptive filters, for each signal class in the learing phase, and classify as a function of their degree of match to each prototype. The underlying matching process can be modelled in terms of cross-correlation between prototype images and the input sample.Study supported by Deutsche Forschungsgemeinschaft grants Re 337/3-3 and Po 121/13-1Deutsche Forschungsgemeinschaft Guest-Professor (Mu 93/103-1) |
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