Particulate matter characterization by gray level co-occurrence matrix based support vector machines

2012 ◽  
Vol 223-224 ◽  
pp. 94-103 ◽  
Author(s):  
K. Manivannan ◽  
P. Aggarwal ◽  
V. Devabhaktuni ◽  
A. Kumar ◽  
D. Nims ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Support Vector Machines ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Occurrence Matrix

A new approach to simulate characterization of particulate matter employing support vector machines

2011 ◽  
Vol 186 (2-3) ◽  
pp. 1254-1262 ◽  
Author(s):  
K. Mogireddy ◽  
V. Devabhaktuni ◽  
A. Kumar ◽  
P. Aggarwal ◽  
P. Bhattacharya
Keyword(s):  
Particulate Matter ◽  
Support Vector Machines ◽  
Support Vector ◽  
New Approach ◽  
Vector Machines

Detection of Closure Features in Gray-Level Images by Using Support Vector Machines

2005 ◽  
Vol 64 (11) ◽  
pp. 923-929
Author(s):  
Mario A. Ibarra-Manzano ◽  
J. Gabriel Avina-Cervantes ◽  
Dora L. Almanza-Ojeda ◽  
Jose Ruiz-Pinales
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Gray Level ◽  
Vector Machines ◽  
Gray Level Images

scholarly journals Denoising and SAR Image Classification with K-SVD Algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 36 ◽  
Author(s):  
D V.R Mohan ◽  
I Rambabu ◽  
B Harish
Keyword(s):  
Support Vector ◽  
Gray Level ◽  
Infrared Sensors ◽  
Sar Images ◽  
Feature Spaces ◽  
Vector Machines ◽  
And Performance ◽  
Occurrence Matrix ◽  
Value Decomposition

Synthetic Aperture Radar (SAR) is not only having the characteristic of obtaining images during all-day, all-weather, but also provides object information which is distinctive from visible and infrared sensors. but, SAR images have more speckles noise and fewer bands. This paper propose a method for denoising, feature extraction and classification of SAR images. Initially the image was denoised using K-Singular Value Decomposition (K-SVD) algorithm. Then the Gray Level Histogram (GLH) and Gray Level Co-occurrence Matrix (GLCM) are used for extraction of features. Secondly, the extracted feature vectors from the first step were combined using the correlation analysis to decrease the dimensionality of the feature spaces. Thirdly, Classification of SAR images was done in Sparse Representations Classification (SRC) and Support Vector Machines (SVMs). The results indicate that the performance of the introduce SAR classification method is good. The above mentioned classifications techniques are enhanced and performance parameters are computed using MATLAB 2014a software.  

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