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Machine Perception MU—Shape Classes

  • Zbigniew LesEmail author
  • Magdalena Les
Chapter
  • 223 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 842)

Abstract

Primary objective of machine perception MU is to construct the symbolic description of the visual content of an image and using this symbolic representation to solve the perceptual problems such as interpretation of perceived images. Symbolically represented visual knowledge provides a level of abstraction at which two otherwise dissimilar domains may look more alike. For example, the concepts of a planet and a ball are quite different, but if both are represented as a circle, it may facilitate analogical retrieval, mapping and transfer. The problem of perception and interpretation of images by application of the IN-perceptual transformation (described in Chap.  6), in order to find the solution to a perceptual problem, is solved within the framework of machine understanding. The machine understanding framework is referring to the human visual system that has a highly developed capability for interpretation of the visual data and detecting many classes of patterns based on statistically significant arrangements of image elements. These classes of patterns and statistically significant arrangements of image elements are called shapes.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The St. Queen Jadwiga Research Institute of UnderstandingToorak, MelbourneAustralia

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