Pattern recognition structured heuristics methods for image processing in mobile robot navigation
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
---|---|
Data(s) |
27/05/2014
27/05/2014
01/12/2010
|
Resumo |
In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE. |
Formato |
4970-4975 |
Identificador |
http://dx.doi.org/10.1109/IROS.2010.5649713 IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings, p. 4970-4975. http://hdl.handle.net/11449/72052 10.1109/IROS.2010.5649713 2-s2.0-78651518109 |
Idioma(s) |
eng |
Relação |
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings |
Direitos |
closedAccess |
Palavras-Chave | #Computer vision #Image segmentation #Mobile robots #Pattern recognition #Artificial Neural Network #HSV space #Image processing technique #Mobile Robot Navigation #Navigation problem #Omnidirectional vision system #Segmentation techniques #SIMULINK environment #Backpropagation algorithms #Imaging systems #Intelligent robots #Navigation #Neural networks |
Tipo |
info:eu-repo/semantics/conferencePaper |