TY - GEN
T1 - Road Signs Segmentation Through Mobile Laser Scanner and Imagery
AU - Flores-Rodríguez, K. L.
AU - González-Barbosa, J. J.
AU - Ornelas-Rodríguez, F. J.
AU - Hurtado-Ramos, J. B.
AU - Ramirez-Pedraza, P. A.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - This work aims to present an urban segmentation to acquire road signs descriptions and annotations. The process implies geometrical characteristics from 3D points clouds (like dimensions, and shape), and visual characteristics from image data (like color wear, and damage) computation. We handle visual and spatial information of the road signs individually to fusion through GPS data in future work. The process for obtaining spatial information from 3D point clouds includes: (i) object segmentation through 3D point cloud density, (ii) use of the retro-reflective attribute of the material to differentiate possible road signs, (iii) plane orientation determination via singular value decomposition, (iv) 2D point cloud projection to geometric shape estimation. The process for getting visual information from images comprises: (i) color segmentation of the road signs in two-parts: border-color and inside-color, (ii) color identification using HSV color model (iii) geometric shape association via contour comparison, (iv) local features extraction and description from semantic data as numbers, characters, and drawings. We chose to work with low rise road signs because the sensors for mobile laser scanning has an elevation angle that delimits the acquisition. We select an experimentation ground truth from the KITTI data set to prove an adequate visual and spatial segmentation.
AB - This work aims to present an urban segmentation to acquire road signs descriptions and annotations. The process implies geometrical characteristics from 3D points clouds (like dimensions, and shape), and visual characteristics from image data (like color wear, and damage) computation. We handle visual and spatial information of the road signs individually to fusion through GPS data in future work. The process for obtaining spatial information from 3D point clouds includes: (i) object segmentation through 3D point cloud density, (ii) use of the retro-reflective attribute of the material to differentiate possible road signs, (iii) plane orientation determination via singular value decomposition, (iv) 2D point cloud projection to geometric shape estimation. The process for getting visual information from images comprises: (i) color segmentation of the road signs in two-parts: border-color and inside-color, (ii) color identification using HSV color model (iii) geometric shape association via contour comparison, (iv) local features extraction and description from semantic data as numbers, characters, and drawings. We chose to work with low rise road signs because the sensors for mobile laser scanning has an elevation angle that delimits the acquisition. We select an experimentation ground truth from the KITTI data set to prove an adequate visual and spatial segmentation.
KW - 3D point clouds
KW - Local features
KW - Mobile laser scanning
KW - Road signs
KW - Spatial feature
KW - Visual features
UR - http://www.scopus.com/inward/record.url?scp=85092918123&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60887-3_33
DO - 10.1007/978-3-030-60887-3_33
M3 - Contribución a la conferencia
AN - SCOPUS:85092918123
SN - 9783030608866
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 376
EP - 389
BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
A2 - Martínez-Villaseñor, Lourdes
A2 - Ponce, Hiram
A2 - Herrera-Alcántara, Oscar
A2 - Castro-Espinoza, Félix A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Y2 - 12 October 2020 through 17 October 2020
ER -