scholarly journals Quaternion matrix singular value decomposition and its applications for color image processing

Author(s):  
Soo-ChangPei ◽  
Ja-Han Chang ◽  
Jian-Jiun Ding

Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).





2018 ◽  
Vol 13 ◽  
pp. 174830181879151
Author(s):  
Qiang Yang ◽  
Huajun Wang

To solve the problem of high time and space complexity of traditional image fusion algorithms, this paper elaborates the framework of image fusion algorithm based on compressive sensing theory. A new image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix is proposed. This proposed algorithm only acts on a small amount of measurement data after compressive sensing sampling, which greatly reduces the number of pixels involved in the fusion and improves the time and space complexity of fusion. In the fusion experiments of full-color image with multispectral image, infrared image with visible light image, as well as multispectral image with full-color image, this proposed algorithm achieved good experimental results in the evaluation parameters of information entropy, standard deviation, average gradient, and mutual information.



1990 ◽  
Author(s):  
Methodi Kovatchev ◽  
Evgeni Mitev ◽  
Rumiana Nedkova ◽  
Methodi Kovatchev ◽  
Evgeni Mitev ◽  
...  


2013 ◽  
Vol 303-306 ◽  
pp. 2122-2125
Author(s):  
Peng Fei Xu ◽  
Hong Bin Zhang ◽  
Xin Feng Wang ◽  
Zheng Yong Yu

This paper looks at the application of Singular Value Decomposition (SVD) to color image compression. Based on the basic principle and characteristics of SVD, combined with the image of the matrix structure. A block SVD-based image compression scheme is demonstrated and the usage feasibility of Block SVD-based image compression is proved.



2007 ◽  
Vol 187 (2) ◽  
pp. 1265-1271 ◽  
Author(s):  
Nicolas Le Bihan ◽  
Stephen J. Sangwine


2009 ◽  
Author(s):  
Yuqing Wang ◽  
Ming Zhu ◽  
Haochen Pang ◽  
Yong Wang


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