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LEARNING SIMPLE TEXTURE DISCRIMINATION FILTERS Rui F. C. Guerreiro Pedro M. Q. Aguiar
 

Summary: LEARNING SIMPLE TEXTURE DISCRIMINATION FILTERS
Rui F. C. Guerreiro Pedro M. Q. Aguiar
Institute for Systems and Robotics, Instituto Superior T´ecnico
Av. Rovisco Pais, 1049-001 Lisboa, Portugal
rfcg@isr.ist.utl.pt, aguiar@isr.ist.utl.pt
ABSTRACT
Current texture analysis methods enable good discrimina-
tion but are computationally too expensive for applications
which require high frame rates. This occurs because they
use redundant calculations, failing in capturing the essence
of the texture discrimination problem. In this paper we use
a learning approach to obtain simple filters for this task.
Although others have proposed learning-based methods,
we are the first to simultaneously achieve discrimination
rates comparable with state-of-the art methods at high
frame rates. We particularize the general methodology
to different filter structures, e.g., rotationally discriminant
filters and rotationally invariant ones. We use Genetic
Algorithms for learning and test our method against state-
of-the-art ones, using the Brodatz album.

  

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

 

Collections: Engineering