QUANTUM SINGULAR VALUE DECOMPOSITION BASED APPROXIMATION ALGORITHM

2010 ◽  
Vol 19 (06) ◽  
pp. 1141-1162 ◽  
Author(s):  
LASZLO GYONGYOSI ◽  
SANDOR IMRE
Keyword(s):  
Singular Value Decomposition ◽  
Fourier Transformation ◽  
Singular Value ◽  
Fourier Domain ◽  
Complex Matrix ◽  
Orthogonal Matrices ◽  
Novel Approach ◽  
Value Decomposition ◽  
Quantum Fourier Transformation ◽  
Ordinary Quantum

Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. The proposed Quantum-SVD algorithm interpolates the non-uniform angles in the Fourier domain. The error of the Quantum-SVD approach is some orders lower than the error given by ordinary Quantum Fourier Transformation. Our Quantum-SVD algorithm is a fundamentally novel approach for the computation of the Quantum Fourier Transformation (QFT) of non-uniform states. The presented Quantum-SVD algorithm is based on the singular value decomposition mechanism, and the computation of Quantum Fourier Transformation of non-uniform angles of a quantum system. The Quantum-SVD approach provides advantages in terms of computational structure, being based on QFT and multiplications.

scholarly journals Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

2010 ◽  
Vol 5 (2) ◽  
pp. 193
Author(s):  
Mourad Moussa Jlassi ◽  
Ali Douik ◽  
Hassani Messaoud
Keyword(s):  
Singular Value Decomposition ◽  
Color Space ◽  
Singular Value ◽  
Space Application ◽  
Color Information ◽  
Novel Approach ◽  
Computer Vision Applications ◽  
Value Decomposition ◽  
Objects Detection ◽  
Background Pixel

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.

An embedding approach using orthogonal matrices of the singular value decomposition for image steganography

2019 ◽  
Vol 79 (11-12) ◽  
pp. 7175-7191
Author(s):  
Hanaa A. Abdallah ◽  
Mohammed Amoon ◽  
Mohiy M. Hadhoud ◽  
Abdalhameed A. Shaalan ◽  
Saleh A. Alshebeili ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Image Steganography ◽  
Orthogonal Matrices ◽  
Value Decomposition

scholarly journals A Novel Security based Image Watermarking using NBP-IWT-DCT-NSVD-GA-PSO Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1600-1609
Keyword(s):  
Singular Value Decomposition ◽  
Image Watermarking ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Image Features ◽  
Singular Value ◽  
The Novel ◽  
Original Image ◽  
Novel Approach ◽  
Value Decomposition

In this novel technique, a modified singular value decomposition named normalized singular value decomposition (NSVD) used for select the original image features to embedding the watermark image into these features. So, the quality of the original image won’t be affected. To select the Normalization Constant of NSVD, the optimization technique used is Genetic Algorithm (GA). In embedding stage, Particle Swarm Optimization (PSO) is used to optimize watermarking constant. Instead of these preliminaries the novel approach also used normalized block processing (NBP) to make the watermarked image more robust to rotation and flipping attacks. The various experiments are conducted on the novel approach to estimate the performance. The experimental results achieved good Robustness for most of the attacks compared to conventional approaches.

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