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Proceedings Paper

Landmine detection using IR image segmentation by means of fractal dimension analysis
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Paper Abstract

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.

Paper Details

Date Published: 4 May 2009
PDF: 10 pages
Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 730317 (4 May 2009); doi: 10.1117/12.819150
Show Author Affiliations
Horacio A. Abbate, Univ. de Buenos Aires (Argentina)
Juliana Gambini, Univ. de Buenos Aires (Argentina)
Claudio Delrieux, Univ. Nacional del Sur (Argentina)
Eduardo H. Castro, Univ. de Buenos Aires (Argentina)


Published in SPIE Proceedings Vol. 7303:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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