Landmine detection using IR image segmentation by means of fractal dimension analysis
This work is concerned with buried landmines detection by long wave infrared images obtained during the heating or cooling of the soil and a segmentation process of the images. The segmentation process is performed by means of a local fractal dimension analysis (LFD) as a feature descriptor. We use...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_0277786X_v7303_n_p_Abbate |
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todo:paper_0277786X_v7303_n_p_Abbate2023-10-03T15:16:39Z Landmine detection using IR image segmentation by means of fractal dimension analysis Abbate, H.A. Gambini, J. Delrieux, C. Castro, E.H. Classification Fractal dimension Infrared imagery Segmentation Box-counting dimension Buried landmines Classification Computational costs Feature descriptors Fractal dimension analysis Infrared imagery IR images K-means method Land mine detection Local fractal dimension Long wave infrared Segmentation Segmentation process Segmentation techniques Bombs (ordnance) Explosives Image segmentation Infrared imaging Mining Partial discharges Fractal dimension This work is concerned with buried landmines detection by long wave infrared images obtained during the heating or cooling of the soil and a segmentation process of the images. The segmentation process is performed by means of a local fractal dimension analysis (LFD) as a feature descriptor. We use two different LFD estimators, box-counting dimension (BC), and differential box counting dimension (DBC). These features are computed in a per pixel basis, and the set of features is clusterized by means of the K-means method. This segmentation technique produces outstanding results, with low computational cost. © 2009 SPIE. Fil:Gambini, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_0277786X_v7303_n_p_Abbate |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Classification Fractal dimension Infrared imagery Segmentation Box-counting dimension Buried landmines Classification Computational costs Feature descriptors Fractal dimension analysis Infrared imagery IR images K-means method Land mine detection Local fractal dimension Long wave infrared Segmentation Segmentation process Segmentation techniques Bombs (ordnance) Explosives Image segmentation Infrared imaging Mining Partial discharges Fractal dimension |
| spellingShingle |
Classification Fractal dimension Infrared imagery Segmentation Box-counting dimension Buried landmines Classification Computational costs Feature descriptors Fractal dimension analysis Infrared imagery IR images K-means method Land mine detection Local fractal dimension Long wave infrared Segmentation Segmentation process Segmentation techniques Bombs (ordnance) Explosives Image segmentation Infrared imaging Mining Partial discharges Fractal dimension Abbate, H.A. Gambini, J. Delrieux, C. Castro, E.H. Landmine detection using IR image segmentation by means of fractal dimension analysis |
| topic_facet |
Classification Fractal dimension Infrared imagery Segmentation Box-counting dimension Buried landmines Classification Computational costs Feature descriptors Fractal dimension analysis Infrared imagery IR images K-means method Land mine detection Local fractal dimension Long wave infrared Segmentation Segmentation process Segmentation techniques Bombs (ordnance) Explosives Image segmentation Infrared imaging Mining Partial discharges Fractal dimension |
| description |
This work is concerned with buried landmines detection by long wave infrared images obtained during the heating or cooling of the soil and a segmentation process of the images. The segmentation process is performed by means of a local fractal dimension analysis (LFD) as a feature descriptor. We use two different LFD estimators, box-counting dimension (BC), and differential box counting dimension (DBC). These features are computed in a per pixel basis, and the set of features is clusterized by means of the K-means method. This segmentation technique produces outstanding results, with low computational cost. © 2009 SPIE. |
| format |
CONF |
| author |
Abbate, H.A. Gambini, J. Delrieux, C. Castro, E.H. |
| author_facet |
Abbate, H.A. Gambini, J. Delrieux, C. Castro, E.H. |
| author_sort |
Abbate, H.A. |
| title |
Landmine detection using IR image segmentation by means of fractal dimension analysis |
| title_short |
Landmine detection using IR image segmentation by means of fractal dimension analysis |
| title_full |
Landmine detection using IR image segmentation by means of fractal dimension analysis |
| title_fullStr |
Landmine detection using IR image segmentation by means of fractal dimension analysis |
| title_full_unstemmed |
Landmine detection using IR image segmentation by means of fractal dimension analysis |
| title_sort |
landmine detection using ir image segmentation by means of fractal dimension analysis |
| url |
http://hdl.handle.net/20.500.12110/paper_0277786X_v7303_n_p_Abbate |
| work_keys_str_mv |
AT abbateha landminedetectionusingirimagesegmentationbymeansoffractaldimensionanalysis AT gambinij landminedetectionusingirimagesegmentationbymeansoffractaldimensionanalysis AT delrieuxc landminedetectionusingirimagesegmentationbymeansoffractaldimensionanalysis AT castroeh landminedetectionusingirimagesegmentationbymeansoffractaldimensionanalysis |
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1807321287255654400 |