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Improving the shape recognition performance of a model with Gabor filter representation

  • Peter Kalocsai
Object Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1296)

Abstract

A recognition model which defines a measure of shape similarity on the direct output of multiscale and multiorientation Gabor filters does not manifest qualitative aspects of human object recognition of contour-deleted images in that: a) it recognizes recoverable and nonrecoverable contour-deleted images equally well whereas humans recognize recoverable images much better, b) it distinguishes complementary feature-deleted images whereas humans do not. Adding some of the known connectivity pattern of the primary visual cortex to the model in the form of extension fields (connections between collinear and curvilinear units) among filters increased the overall recognition performance of the model and: a) boosted the recognition rate of the recoverable images far more than the nonrecoverable ones, b) increased the similarity of complementary feature-deleted images, but not part-deleted ones, more closely corresponding to human psychophysical results. Interestingly, performance was approximately equivalent for narrow (±15°) and broad (±90°) extension fields.

Keywords

Baseline Model Recognition Performance Extension Field Gabor Filter Connectivity Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Peter Kalocsai
    • 1
  1. 1.University of Southern CaliforniaUSA

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