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Computer Vision and Fuzzy-Neural Systems
  • Dr. Arun D Kulkarni, University of Texas at Tyler
Computer vision deals with extracting meaningful descriptions of physical objects from images. Computer vision has many practical applications such as remote sensing, medical image processing, robot vision, military reconnaissance, mineral exploration, cartography, forestry, etc. Recent developments in neural networks and fuzzy logic have changed the computer vision field dramatically. During the past few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. Neural networks provide algorithms for learning and are modeled after the physical architecture of the brain. Fuzzy logic deals with issues such as reasoning at the semantic or linguistic level and is based on the way brain deals with inexact information. Consequently, the two technologies complement each other. A variety of fuzzy-neural network models have been used in computer vision. This book deals with the topic of fuzzy-neural systems as applied to computer vision. The book provides exercises at the end of each chapter, and it can be used as a textbook for a course in computer vision at senior undergraduate or master degree level. The book also provides engineers, scientists, researchers, and students involved in computer vision a comprehensive, well-organized, up-to-date overview of recent techniques used in computer vision. The book is the outgrowth of my lecture notes in various classes that I taught at The University of Texas at Tyler. The material in the book is well tested in the classroom. It also has been published as journal articles and has been presented at various professional meetings.
  • fuzzy-neural systems
Publication Date
Prentice Hall
Citation Information
Kulkarni, A. D. (2001). Computer Vision and Fuzzy Neural Systems. Saddle River, NJ.: Prentice Hall.