Digital Image Processing using Singular Value Decomposition

2019 ◽  
Author(s):  
Jay Prakash Pandey ◽  
Lokendra Singh Umrao
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Singular Value ◽  
Value Decomposition

Application Of Singular Value Decomposition To Digital Image Processing

1990 ◽  
Author(s):  
Methodi Kovatchev ◽  
Evgeni Mitev ◽  
Rumiana Nedkova ◽  
Methodi Kovatchev ◽  
Evgeni Mitev ◽  
...  
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Digital Image Processing ◽  
Digital Image ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

2019 ◽  
Vol 9 (2) ◽  
pp. 19-26
Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Parameter Determination ◽  
Iris Image ◽  
Determination System ◽  
Value Decomposition

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).

scholarly journals Detection of Surface Defects on Steel Strips Based on Singular Value Decomposition of Digital Image

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qianlai Sun ◽  
Jianghui Cai ◽  
Zhiyi Sun
Keyword(s):  
Image Segmentation ◽  
Singular Value Decomposition ◽  
Digital Image ◽  
Manufacturing Industry ◽  
Surface Defects ◽  
Sudden Change ◽  
Singular Value ◽  
Steel Strips ◽  
Novel Method ◽  
Value Decomposition

Image segmentation technology has been widely used to detect the surface defects in metal industries effectively. In some fields of the manufacturing industry, the determination of defects is more concerned than the accurate location and shape of defects. However, most of current image segmentation algorithms are complex or have difficulty determining the defect. This paper presents a novel method for determining and roughly locating the surface defects of steel strips based on Singular Value Decomposition. The method has no need of image segmentation. The gray level matrix of a digital image is projected on its singular vectors obtained by Singular Value Decomposition. A defect is reflected as a sudden change on the projections. Therefore, the defects can be determined and roughly located according to the sudden changes. The experimental results suggest that this method is valid and convenient for determining the surface defects directly.

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