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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Detalles Bibliográficos
Autor principal: Gambini, María Juliana
Publicado: 2009
Materias:
Classification
Fractal dimension
Infrared imagery
Segmentation
Box-counting dimension
Buried landmines
Computational costs
Feature descriptors
Fractal dimension analysis
IR images
K-means method
Land mine detection
Local fractal dimension
Long wave infrared
Segmentation process
Segmentation techniques
Bombs (ordnance)
Explosives
Image segmentation
Infrared imaging
Mining
Partial discharges
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v7303_n_p_Abbate
http://hdl.handle.net/20.500.12110/paper_0277786X_v7303_n_p_Abbate
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_0277786X_v7303_n_p_Abbate
record_format dspace
spelling paper:paper_0277786X_v7303_n_p_Abbate2025-07-30T18:04:18Z Landmine detection using IR image segmentation by means of fractal dimension analysis Gambini, María Juliana 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. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v7303_n_p_Abbate 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
Gambini, María Juliana
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.
author Gambini, María Juliana
author_facet Gambini, María Juliana
author_sort Gambini, María Juliana
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
publishDate 2009
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v7303_n_p_Abbate
http://hdl.handle.net/20.500.12110/paper_0277786X_v7303_n_p_Abbate
work_keys_str_mv AT gambinimariajuliana landminedetectionusingirimagesegmentationbymeansoffractaldimensionanalysis
_version_ 1840321098421370880

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