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Advanced neural network clustering t...

Karaszi, Zoltan.

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Advanced neural network clustering techniques for liquid crystal texture classification.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advanced neural network clustering techniques for liquid crystal texture classification./
作者:	Karaszi, Zoltan.
面頁冊數:	71 p.
附註:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
標題:	Chemistry, Physical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1555292
ISBN:	9781303874253

Advanced neural network clustering techniques for liquid crystal texture classification.
Karaszi, Zoltan.

Advanced neural network clustering techniques for liquid crystal texture classification. - 71 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.C.Sc.)--Kent State University, 2013.

This Master Thesis presents a new method of analyzing and classifying liquid crystal textures, using feed-forward neural networks and different clustering techniques. Liquid crystal phases are generally identified by human experts by polarizing optical microscopy observations of textures, based on typical defects, the smoothness or sharpness of domains and the birefringence colors of the films. The thesis aims to establish a novel algorithmic technique for liquid crystal texture analysis and classification. Using image analyzing software, a characterization vector with 22 parameters is extracted for each texture. Advanced clustering algorithms are used to classify textures based on those characteristic parameters. Furthermore, a ranking of the measurements is proposed to refine the accuracy of classification. The proposed methodology will lead to a reliable and simple technique for the physical investigation of liquid crystal materials.

ISBN: 9781303874253Subjects--Topical Terms:

560527
Chemistry, Physical.

Advanced neural network clustering techniques for liquid crystal texture classification.
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245 1 0 $a Advanced neural network clustering techniques for liquid crystal texture classification.
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500 $a Source: Masters Abstracts International, Volume: 52-06.
500 $a Advisers: Feodor F. Dragan; Antal I. Jakli.
502 $a Thesis (M.C.Sc.)--Kent State University, 2013.
520 $a This Master Thesis presents a new method of analyzing and classifying liquid crystal textures, using feed-forward neural networks and different clustering techniques. Liquid crystal phases are generally identified by human experts by polarizing optical microscopy observations of textures, based on typical defects, the smoothness or sharpness of domains and the birefringence colors of the films. The thesis aims to establish a novel algorithmic technique for liquid crystal texture analysis and classification. Using image analyzing software, a characterization vector with 22 parameters is extracted for each texture. Advanced clustering algorithms are used to classify textures based on those characteristic parameters. Furthermore, a ranking of the measurements is proposed to refine the accuracy of classification. The proposed methodology will lead to a reliable and simple technique for the physical investigation of liquid crystal materials.
590 $a School code: 0101.
650 4 $a Chemistry, Physical. $3 560527
650 4 $a Computer Science. $3 626642
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690 $a 0984
710 2 $a Kent State University. $b Computer Science. $3 2106353
773 0 $t Masters Abstracts International $g 52-06(E).
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791 $a M.C.Sc.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1555292