Granular computing in computer image perception: basic issues and glass box models

  • Authors:

  • Sergey A. Butenkov;Vitaly V. Krivsha;Al Dhouyani Saud

  • Affiliations:

  • Taganrog State University of Radio Engineering, Computer Science and Artifical Intelligence Laboratory, Taganrog, Russian Federation;Taganrog State University of Radio Engineering, Computer Science and Artifical Intelligence Laboratory, Taganrog, Russian Federation;Taganrog State University of Radio Engineering, Computer Science and Artifical Intelligence Laboratory, Taganrog, Russian Federation

  • Venue:
  • AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

The Granular Computing (GrC) emerges as a new multidisciplinary study and has received much attention in recent years. It is argued that GrC is more about the theoretical studies and a practical methodology of problem solving. By effectively using levels of granularity, GrC theory provides systematic practical way to analyze and represent the real world information. The most complicated problems are the image processing, analyze etc. Follow to very common terminology introduced by L. Zadeh we can notify the full collection of related problems in computer vision as "visual perception". There are three main fulcrum to provide the effective techniques for GrC algorithms. At first, we will use the General system theory to represent and circumscribe the image models. Second, we must use the Soft Computing guide principle to achieve the computational complexity reduction. At least, we must take into consideration all uncertainty factors (clutter), presented in real world images. Like the rock-climber, we can bear on the mentioned handholds and achieve the methodology for mathematically homogeneous process of visual perception.