Machine Perception MU—Shape Classes
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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.
- 1.Arnheim R (1970) Visual thinking. Faber and Faber, LondonGoogle Scholar
- 5.Les Z, Les M (2004) Shape understanding system: understanding of the complex thin object. In: IASTED conference on computer graphics and imaging, Kauai, HawaiiGoogle Scholar
- 6.Les Z, Les M (2003) Shape understanding system: understanding of the cyclic object. In: IASTED conference on signal processing and imaging, Rhodos, GreeceGoogle Scholar
- 8.Les Z, Les M (2003) Understanding of the concave-complex object. In: IASTED conference on visualization, imaging, and image processing, Benalmadena, SpainGoogle Scholar
- 9.Les Z, Les M (2018) Machine understanding—testing visual understanding ability of machine: the visual intelligence test. Int J Underst 7Google Scholar