scholarly journals WDFB Digital Watermarking Method Based on Multiscale Geometrie Analysis

2012 ◽  
Vol 6-7 ◽  
pp. 1145-1149
Author(s):  
Gui Ming Shao ◽  
Zhi Hua Hu
Keyword(s):  
Image Analysis ◽  
Wavelet Transform ◽  
Digital Watermarking ◽  
Median Filtering ◽  
Watermark Image ◽  
Original Image ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Strong Robustness ◽  
The One

Combined with the human visual system and proposes a digital watermarking algorithm based WDFB domain. The algorithm is not the original image and watermark image WDFB coefficient directly superimposed, but the original image WDFB coefficient after pretreatment by the addition criteria to implement the embedded watermark. The experimental results show that the algorithm has strong robustness to shear, median filtering, noise and JPEG compression attacks. WDFB transform a new image analysis method is proposed for the lack of wavelet transform, but the watermarking method that there exist inadequacies; on the one hand, the need to use the original extract the watermark image should be along the zero-watermark the direction of the watermarking algorithm improvements.

Research on Digital Watermarking Algorithm Based on Discrete Wavelet Transform and Singular Value Decomposition

2015 ◽  
Vol 713-715 ◽  
pp. 1843-1846
Author(s):  
Yan Yu Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Watermarking ◽  
Singular Values ◽  
Discrete Wavelet ◽  
Watermark Image ◽  
Original Image ◽  
Host Image ◽  
Important Means ◽  
Value Decomposition

Digital watermarking technology has become an important means of integrity and respect for people's authenticity, as well as users of copyright and intellectual property security and other interests such as the protection of digital works. In this paper, we proposed a watermarking algorithm based on discrete wavelet transform (DWT) of image and singular valued composition (SVD). The original image is decomposed with DWT,the watermarking image is decomposed with SVD after chaotic scrambling,and then the singular values of watermarking are embedded into some coefficients of decomposed original image. In this algorithm, after decomposing the original host image into four bands, we apply the SVD to watermark image,and modify DWT coefficients of the host image with the singular values of the watermark image. The outstanding features of the proposed algorithm are that it provides larger watermarking capacity and is more robust than others.

scholarly journals Automated image analysis method for oil-release test of lipid-based materials

MethodsX ◽  
2021 ◽  
pp. 101447
Author(s):  
Fabio Valoppi ◽  
Petri Lassila ◽  
Ari Salmi ◽  
Edward Haeggström
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Release Test

A Computerized Image Analysis Method for Measuring Elastic Tissue.

1989 ◽  
Vol 93 (3) ◽  
pp. 358-362 ◽  
Author(s):  
Thomas J. Flotte ◽  
Johanna M. Seddon ◽  
Yuqing Zhang ◽  
Robert J. Glynn ◽  
Kathleen M. Egan ◽  
...  
Keyword(s):  
Image Analysis ◽  
Elastic Tissue ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Computerized Image Analysis

ASSESSMENT OF OSTEOARTHRITIS INITIATIVE–KELLGREN AND LAWRENCE SCORING PROJECTS QUALITY USING COMPUTER ANALYSIS

2010 ◽  
Vol 13 (04) ◽  
pp. 197-201 ◽  
Author(s):  
Lior Shamir ◽  
David T. Felson ◽  
Luigi Ferrucci ◽  
Ilya G. Goldberg
Keyword(s):  
Image Analysis ◽  
Gold Standard ◽  
Population Studies ◽  
Detection Accuracy ◽  
X Rays ◽  
Analysis Method ◽  
Image Analysis Method ◽  
X Ray ◽  
Osteoarthritis Initiative ◽  
Kellgren And Lawrence

The detection of knee osteoarthritis (OA) is a subjective task, and even two highly experienced and well-trained readers might not always agree on a specific case. This problem is noticeable in OA population studies, in which different scoring projects provide significantly different scores for the same knee X-rays. Here we propose a method for quantitative assessment and comparison of knee X-ray scoring projects in OA population studies. The method works by applying an image analysis method that automatically detects OA in knee X-ray images, and comparing the consistency of the scores when using each of the scoring projects as "gold standard." The method was applied to compare the osteoarthritis initiative (OAI) clinic reading derived Kellgren and Lawrence (K&L) scores to central reading, and showed that when using the derived K&L scores the automatic image analysis method was able to accurately differentiate between healthy joints and moderate OA joints in ~70% of the cases. When the OAI central reading scores were used as gold standard, the detection accuracy was elevated to ~77%. These results show that the OAI central readings scores are more consistent with the X-rays, indicating that the central reading better reflects the radiographic features associated with OA, compared to the OAI K&L scores derived from clinic readings.

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