東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A set-preserving approach for develo...

Krebsbach, Stephen James.

Linked to FindBook

Google Book

Amazon

博客來

A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images./
Author:	Krebsbach, Stephen James.
Description:	96 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6728.
Contained By:	Dissertation Abstracts International66-12B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200199
ISBN:	0542470349

A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images.
Krebsbach, Stephen James.

A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images. - 96 p.

Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6728.

Thesis (Ph.D.)--North Dakota State University, 2005.

As the use of Remotely Sensed Satellite (RSS) images has grown and diversified, the need to protect the investment of those who have born the cost of collecting and pre-processing the data for distribution has become an area of interest in the context of Information Assurance, in particular, ownership rights protection. Research into the possible use of Invisible Digital Watermarking (IDW) methods for this purpose has established that, unlike traditional IDW methods, a near-losses constraint must be enforced. Attempts to develop near-lossless IDW methods have yet to yield acceptable results and have brought into question the efficacy of using IDW methods with RSS images.

ISBN: 0542470349Subjects--Topical Terms:

626642
Computer Science.

A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images.
LDR:02396nmm 2200289 4500 001 1818821
005 20061003090440.5
008 130610s2005 eng d
020 $a 0542470349
035 $a (UnM)AAI3200199
035 $a AAI3200199
040 $a UnM $c UnM
100 1 $a Krebsbach, Stephen James. $3 1908130
245 1 2 $a A set-preserving approach for developing invisible digital watermarking methods for remotely sensed satellite images.
300 $a 96 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6728.
500 $a Major Professor: William Perrizo.
502 $a Thesis (Ph.D.)--North Dakota State University, 2005.
520 $a As the use of Remotely Sensed Satellite (RSS) images has grown and diversified, the need to protect the investment of those who have born the cost of collecting and pre-processing the data for distribution has become an area of interest in the context of Information Assurance, in particular, ownership rights protection. Research into the possible use of Invisible Digital Watermarking (IDW) methods for this purpose has established that, unlike traditional IDW methods, a near-losses constraint must be enforced. Attempts to develop near-lossless IDW methods have yet to yield acceptable results and have brought into question the efficacy of using IDW methods with RSS images.
520 $a In this dissertation, a new set-preserving approach that defines a new near-lossless constraint is presented. The new set-preserving constraint is used to help develop a new IDW method for use with RSS images. Experimental results show that this new IDW method is optimal in the reduction of distortion when used as input to tuple-centric analytical applications and can be near-optimal in reducing distortion when used as input for the most common analytical applications above a given fixed cropping region size while still being able to withstand the most common attack against the watermark. Experimental results also show a significant improvement in the reduction of misclassifications occurring as a result of watermarking compared to published results.
590 $a School code: 0157.
650 4 $a Computer Science. $3 626642
650 4 $a Remote Sensing. $3 1018559
690 $a 0984
690 $a 0799
710 2 0 $a North Dakota State University. $3 1021724
773 0 $t Dissertation Abstracts International $g 66-12B.
790 1 0 $a Perrizo, William, $e advisor
790 $a 0157
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200199