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Detection and Localization of Linear...

Matz, Sean.

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Detection and Localization of Linear Features Based on Image Processing Methods.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Detection and Localization of Linear Features Based on Image Processing Methods./
作者:	Matz, Sean.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	158 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-12.
Contained By:	Dissertations Abstracts International81-12.
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27834929
ISBN:	9798645446246

Detection and Localization of Linear Features Based on Image Processing Methods.
Matz, Sean.

Detection and Localization of Linear Features Based on Image Processing Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 158 p.

Source: Dissertations Abstracts International, Volume: 81-12.

Thesis (Ph.D.)--The Claremont Graduate University, 2020.

This item must not be sold to any third party vendors.

In this work, the general problem of the detection of features in images is considered. One of the methods, the orientation detection of lines, utilized the Radon transform (sinogram) of an image to detect lines at different angles in an image. The line thickness algorithm was generated by finding a pattern formed by particular lines in an image. The filtering of reconstructed images dealt with the removal of blur and other artifacts that arose in the course of inverting the Radon transform of an image to attempt to obtain the original image.

ISBN: 9798645446246Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Filtered images

Detection and Localization of Linear Features Based on Image Processing Methods.
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500 $a Source: Dissertations Abstracts International, Volume: 81-12.
500 $a Advisor: Chugunova, Marina.
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506 $a This item must not be sold to any third party vendors.
520 $a In this work, the general problem of the detection of features in images is considered. One of the methods, the orientation detection of lines, utilized the Radon transform (sinogram) of an image to detect lines at different angles in an image. The line thickness algorithm was generated by finding a pattern formed by particular lines in an image. The filtering of reconstructed images dealt with the removal of blur and other artifacts that arose in the course of inverting the Radon transform of an image to attempt to obtain the original image.
590 $a School code: 0047.
650 4 $a Applied mathematics. $3 2122814
653 $a Filtered images
653 $a Fourier slice theorem
653 $a Fourier transform
653 $a Image processing
653 $a Orientation detection
653 $a Radon transform
690 $a 0364
710 2 $a The Claremont Graduate University. $b Institute of Mathematical Sciences. $3 3544226
773 0 $t Dissertations Abstracts International $g 81-12.
790 $a 0047
791 $a Ph.D.
792 $a 2020
793 $a English
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