Abstract
International audience; The aim of this paper is to detect and follow the pipeline in sonar imagery. This work is performed in two steps. The first is to split an image (first experiment) or an transformed line image of pipeline image (second experiment) into regions of uniform texture using the Gray Level Co-occurrence Matrix Method (GLCM). The second addresses the unsupervised learning method based on the Artificial Neural Networks (Self-Organizing Map or SOM) used for determining the comparative model of pipeline from the image. To increase the performance of SOM, we propose a penalty function based on data histogram visualization for detecting the position of pipeline. After a brief review of both techniques (GLCM and SOM), we will present our methods and some results from several experiments on the real world data set.
Original document
The different versions of the original document can be found in:
- https://www.researchgate.net/profile/Bruno_Jouvencel/publication/4065819_Using_Self-Organizing_Maps_Approach_to_Pipeline_Localization/links/00b49518898266504d000000.pdf,
- https://hal-lirmm.ccsd.cnrs.fr/docs/00/14/17/03/PDF/Ocean2003_b.PDF,
- https://ieeexplore.ieee.org/document/1282919,
- https://hal-lirmm.ccsd.cnrs.fr/docs/00/19/19/31/PDF/D117.PDF,
- https://academic.microsoft.com/#/detail/1491751277
Document information
Published on 01/01/2003
Volume 2003, 2003
DOI: 10.1109/oceans.2003.1282919
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?