NDLI: Feature extraction and shape classification of 2-D polygons using a neural network

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Feature extraction and shape classification of 2-D polygons using a neural network

Content Provider	IEEE Xplore Digital Library
Author	Jamison, T.A. Schalkoff, R.J.
Copyright Year	1989
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA (Jamison, T.A.; Schalkoff, R.J.)
Abstract	A neural-network architecture for classification of 2-D polygonal objects is developed. The architecture is restricted to simple and viable neural mechanisms, based on those known to exist in biological neural systems. Some low-level parts of the architecture are based on the boundary contour system model of S. Grossbert (1987). The object recognition subsystem exhibits aspects of both structural/relational and decision-theoretic pattern recognition. Two key aspects of the architecture are: (1) the ability to extract and aggregate features in a hierarchical manner, such that a large number of object classes and subclasses can be recognized; and (2) the ability to transition from location-dependent feature information to location-independent feature information, such that rotational-, scale-, and translational-invariant classification is possible. Computer simulation results for one sample object are detailed.
Starting Page	953
Ending Page	958
File Size	708563
Page Count	6
File Format	PDF
DOI	10.1109/SECON.1989.132550
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-04-09
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Shape Neural networks Computer architecture Image segmentation Biology computing Humans Biological system modeling Animal structures Image processing
Content Type	Text
Resource Type	Article

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