An Image Hiding Method Based on Correlation Analysis and Image Fusion

2005 ◽  
Author(s):  
Liu Bin ◽  
Li Zhitang ◽  
Li Yao
Keyword(s):  
Correlation Analysis ◽  
Image Fusion ◽  
Image Hiding

A novel image hiding approach based on correlation analysis for secure multimodal biometrics

2010 ◽  
Vol 33 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Miao Qi ◽  
Yinghua Lu ◽  
Ning Du ◽  
Yinan Zhang ◽  
Chengxi Wang ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Multimodal Biometrics ◽  
Image Hiding

Monotonic correlation analysis of image quality measures for image fusion

2008 ◽  
Author(s):  
Lance M. Kaplan ◽  
Stephen D. Burks ◽  
Richard K. Moore ◽  
Quang Nguyen
Keyword(s):  
Image Quality ◽  
Correlation Analysis ◽  
Image Fusion ◽  
Quality Measures ◽  
Image Quality Measures

CONTENT-BASED BIOMETRIC IMAGE HIDING APPROACH

2011 ◽  
Vol 25 (04) ◽  
pp. 565-581 ◽  
Author(s):  
MIAO QI ◽  
JUN KONG ◽  
YINGHUA LU ◽  
NING DU ◽  
ZHIQIANG MA
Keyword(s):  
Correlation Analysis ◽  
Information Hiding ◽  
Principal Component ◽  
Identification Accuracy ◽  
Cover Image ◽  
Biometric Identification ◽  
Hidden Information ◽  
Biometric Data ◽  
Secret Image ◽  
Image Hiding

Recently, the use of information hiding techniques to protect biometric data has been an active topic. This paper proposes a novel image hiding approach based on correlation analysis to protect network-based transmitted biometric image for identification. Firstly, the correlation between the biometric image and the cover image is analyzed using principal component analysis (PCA) and genetic algorithm (GA). The purpose of correlation analysis is to enable the cover image to represent the secret image in content as much as possible, not just as a carrier of hidden information. Then, the unrepresented part of the biometric image, as the secret image, is encrypted and hidden into the middle-significant-bit plane (MSB) of the cover image redundantly. Extensive experimental results demonstrate that the proposed hiding approach not only gains good imperceptibility, but also resists some common attacks validated by the biometric identification accuracy.

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

1528: Improved Sentinel Node Detection and Pre-Operative Planning in Prostate Cancer by Image Fusion of CT/MRI and Spect

2005 ◽  
Vol 173 (4S) ◽  
pp. 414-414
Author(s):  
Frank G. Fuechsel ◽  
Agostino Mattei ◽  
Sebastian Warncke ◽  
Christian Baermann ◽  
Ernst Peter Ritter ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Sentinel Node ◽  
Image Fusion ◽  
Sentinel Node Detection ◽  
Operative Planning

The German Strengths and Difficulties Questionnaire (SDQ)

2010 ◽  
Vol 26 (4) ◽  
pp. 256-262 ◽  
Author(s):  
Ulrike Petermann ◽  
Franz Petermann ◽  
Ina Schreyer
Keyword(s):  
Correlation Analysis ◽  
Teacher Ratings ◽  
Screening Instrument ◽  
Preschool Age ◽  
Strengths And Difficulties Questionnaire ◽  
Preschool Age Children ◽  
Discriminant Analyses ◽  
Strengths And Difficulties ◽  
Behavioral Attributes ◽  
And Behavior

The Strengths and Difficulties Questionnaire (SDQ) is a screening instrument that addresses positive and negative behavioral attributes of children and adolescents. Although this questionnaire has been used in Germany to gather information from parents and teachers of preschoolers, few studies exist that verify the validity of the German SDQ for this age. In the present study, teacher ratings were collected for 282 children aged 36 to 60 months (boys = 156; girls = 126). Likewise, teacher ratings were collected with another German checklist for behavior problems and behavior disorders at preschool age (Verhaltensbeurteilungsbogen für Vorschulkinder, VBV 3–6). Moreover, children’s developmental status was assessed. Evaluation included correlation analysis as well as canonical correlation analysis to assess the multivariate relationship between the set of SDQ variables and the set of VBV variables. Discriminant analyses were used to clarify which SDQ variables are useful to differentiate between children with or without developmental delay in a multivariate model. The results of correlation and discriminant analyses underline the validity of the SDQ for preschoolers. According to these results, the German teacher SDQ is recommended as a convenient and valid screening instrument to assess positive and negative behavior of preschool age children.

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