International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 7, No. 4, 8 Mar. 2015
Cover page and Table of Contents: PDF (size: 894KB)
Intelligent Geometric Classification of Irregular Patterns via Probabilistic Neural Network
Full Text (PDF, 894KB), PP.19-27
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Author(s)
Sogand Hoshyarmanesh
1,*
Mohammadreza Fathikazerooni
2
Mohsen Bahrami
1
Index Terms
Pattern Recognition, Neural Network, Feature Extraction, Distance Histogram
Abstract
This paper deals with interpretation of patterns via neural networks under organization and classification approaches. Fifty different groups of images including geometric shapes, mechanical instruments, machines, animals, fruits, and other classes of samples are classified here in two successive steps. Each primary category is divided into three different sub-groups. The purpose is identifying the class and sub-class of each input sample. Nowadays, industry and manufacturing are moving towards automation; hence accurate description of photos results in a myriad of industrial, security, and medical applications and takes a pressing part in artificial intelligence's progression. Intelligent interpretation of structure's design in CNC machine eventuates in autonomous selection of cutting tools by which any structure can easily be manufactured. Anyhow, this paper comes up with a pattern interpretation method to be applied in submarine detection purposes. Remotely operated vehicles (ROV) are used to detect and survey oil pipelines and underwater marine structures, so mentioned neural network classification is a practicable tool for detection mechanism and avoiding obstacles in ROVs.
Cite This Paper
Sogand Hoshyarmanesh, Mohammadreza Fathikazerooni, Mohsen Bahrami,"Intelligent Geometric Classification of Irregular Patterns via Probabilistic Neural Network", IJIGSP, vol.7, no.4, pp.19-27, 2015. DOI: 10.5815/ijigsp.2015.04.02
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