國立虎尾科技大學 |

Clustering and Segmentation with Application in Document Image Processing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Clustering and Segmentation with Application in Document Image Processing./
作者:	Xue, Haitao.
面頁冊數:	1 online resource (85 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355614152

Clustering and Segmentation with Application in Document Image Processing.
Xue, Haitao.

Clustering and Segmentation with Application in Document Image Processing. - 1 online resource (85 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2017.

Includes bibliographical references

In this dissertation, we introduce a set of algorithms for document image process- ing, which are in the research area of color clustering and binarization.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355614152Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Clustering and Segmentation with Application in Document Image Processing.
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100 1 $a Xue, Haitao. $3 1194233
245 1 0 $a Clustering and Segmentation with Application in Document Image Processing.
264 0 $c 2017
300 $a 1 online resource (85 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Charles A. Bouman.
502 $a Thesis (Ph.D.)--Purdue University, 2017.
504 $a Includes bibliographical references
520 $a In this dissertation, we introduce a set of algorithms for document image process- ing, which are in the research area of color clustering and binarization.
520 $a Color quantization algorithms are used to select a small number of colors that can accurately represent the content of a particular image. In this research, we introduce a novel color quantization algorithm which is based on the minimization of a modified Lp norm rather than the more traditional L2 norm associated with mean square error (MSE) [1]. We demonstrate that the Lp optimization approach has two advantages. First, it produces more accurate perceived quality results, especially for important colors in small regions; and second, the norm's value can be used as an effective criterion for selecting the minimum number of colors necessary to achieve accurate representation of the image.
520 $a Binarization algorithms are used to create a binary representation of a raster document image, typically with the intent of identifying text and separating it from background content. In this work, we propose a binarization algorithm via one-pass local classification [2]. The algorithm first generates the initial binarization results by local thresholding, then corrects the results using a one-pass local classification strategy, followed by the process of component inversion. The experimental results demonstrate that our algorithm achieves a much lower binarization error rate than other popular binarization/thresholding algorithms. It is also demonstrated that the proposed algorithm achieves a somewhat lower binarization error rate than the state-of-the-art algorithm COS [3], while requiring significantly less computation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Electrical and Computer Engineering. $3 1148521
773 0 $t Dissertation Abstracts International $g 79-07B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10685608 $z click for full text (PQDT)