An Embedded Classifier for Mobile Robot Localization Using Support Vector Machines and Gray-Level Co-occurrence Matrix

2018 ◽  
pp. 201-213
Author(s):  
Fausto Sampaio ◽  
Elias T. Silva ◽  
Lucas C. da Silva ◽  
Pedro P. Rebouças Filho
Keyword(s):  
Support Vector Machines ◽  
Mobile Robot ◽  
Support Vector ◽  
Robot Localization ◽  
Gray Level ◽  
Vector Machines ◽  
Mobile Robot Localization ◽  
Occurrence Matrix

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

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.  

Nonsingular Terminal Sliding Mode Control of Mobile Robot Using Support Vector Machines

2012 ◽  
Vol 190-191 ◽  
pp. 705-709
Author(s):  
Xi Ru Wu ◽  
Yao Nan Wang
Keyword(s):  
Support Vector Machines ◽  
Sliding Mode Control ◽  
Mobile Robot ◽  
Sliding Mode ◽  
Support Vector ◽  
Terminal Sliding Mode Control ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
Nonsingular Terminal Sliding Mode ◽  
Vector Machines

In this paper, nonsingular terminal sliding mode control strategies are originally presented for mobile robot in the presence of uncertainties and disturbances. The support vector machines is used to approximate an unknown controlled system from the strategic manipulation of the model errors. Based on the Lyapunov stability theory, it is shown that the proposed controller can prove the stability of the closed-loop system and guarantee tracking performance of robotic system. Finally, simulation results validate the superior control performance of the proposed control method.

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization
Sign in / Sign up

Export Citation Format

Share Document