Intelligent Geometric Classification of Irregular Patterns via Probabilistic Neural Network

Intelligent Geometric Classification of Irregular Patterns via Probabilistic Neural Network

Автор: Sogand Hoshyarmanesh, Mohammadreza Fathikazerooni, Mohsen Bahrami

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 4 vol.7, 2015 года.

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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.

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Pattern Recognition, Neural Network, Feature Extraction, Distance Histogram

Короткий адрес: https://sciup.org/15013549

IDR: 15013549

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