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.

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.

scholarly journals Robust Video Watermarking using Tchebichef Transform and Singular Value Decomposition on the Selected Frame Based YCbCr Color Space

2020 ◽  
Vol 13 (6) ◽  
pp. 432-441
Author(s):  
Andik Setyono ◽  
◽  
De Setiadi ◽  
Keyword(s):  
Singular Value Decomposition ◽  
Color Space ◽  
Singular Value ◽  
Video Watermarking ◽  
Average Value ◽  
Ycbcr Color Space ◽  
Watermark Embedding ◽  
Value Decomposition ◽  
Linear Congruential ◽  
Measuring Tool

Watermarking is a copyright authentication technique. This research proposes a robust watermarking method with a combination of Tchebichef transformation and singular value decomposition (SVD). To maintain imperceptibility, embedding is done on one of the selected frames. Frames are randomly selected to increase watermark security. Frame selection is based on two integer keys processed by a linear congruential generator (LCG). The selected frame is then converted to its color space from RGB to YCbCr. Y channel (luminance) was selected to be processed by Tchebichef transformation based on block 8 × 8, the coefficient 0.0 for each block of the transformed results was selected and collected on a matrix. This matrix is then transformed with SVD and a singular matrix is selected for watermark embedding, this method is done to increase robustness. Based on the test results, the imperceptibility value is very good with an average value of 50.952dB, based on the PSNR as a measuring tool. Whereas in the robustness aspect, a value of 0.927 is generated based on the results of the measurement of the correlation between the watermark and the original watermark, where these results are the average extraction results without and with various attacks.

A Simple Modelling Method for Deflection of Circular Plates Under Impulsive Loading using Dimensionless Analysis and Singular value Decomposition

2010 ◽  
Vol 26 (3) ◽  
pp. 355-361 ◽  
Author(s):  
H. Gharababaei ◽  
N. Nariman-zadeh ◽  
A. Darvizeh
Keyword(s):  
Experimental Data ◽  
Singular Value Decomposition ◽  
Impulsive Loading ◽  
Singular Value ◽  
Circular Plates ◽  
Dimensionless Analysis ◽  
Novel Approach ◽  
Complex Process ◽  
Value Decomposition ◽  
Svd Method

AbstractA novel approach of numerical modelling using input-output experimental data pairs is presented for deflection-thickness ratio of circular plates subjected to impulse loading. In this way, singular value decomposition (SVD) method is used in conjunction with dimensionless parameters incorporated in such complex process. The closed-form obtained model shows very good agreement with some testing experimental data pairs which have been unforeseen during the training process. Moreover, two modifications are consequently suggested for some similar models already proposed in previous works. The approach of this paper can generally be applied to model very complex real-world processes using appropriate experimental data.

scholarly journals Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Feng-Jih Wu ◽  
Chih-Ju Chou ◽  
Ying Lu ◽  
Jarm-Long Chung
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference ◽  
Singular Value ◽  
Time Characteristic ◽  
Characteristic Curves ◽  
Inference System ◽  
Novel Approach ◽  
Curve Fit ◽  
Overcurrent Relays ◽  
Value Decomposition

This paper presents a practical and effective novel approach to curve fit electromechanical (EM) overcurrent (OC) relay characteristics. Based on singular value decomposition (SVD), the curves are fitted with equation in state space under modal coordinates. The relationships between transfer function and Markov parameters are adopted in this research to represent the characteristic curves of EM OC relays. This study applies the proposed method to two EM OC relays: the GE IAC51 relay with moderately inverse-time characteristic and the ABB CO-8 relay with inverse-time characteristic. The maximum absolute values of errors of hundreds of sample points taken from four time dial settings (TDS) for each relay between the actual characteristic curves and the corresponding values from the curve-fitting equations are within the range of 10 milliseconds. Finally, this study compares the SVD with the adaptive network and fuzzy inference system (ANFIS) to demonstrate its accuracy and identification robustness.

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.

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