Ms. Maria Elena Buemi Profile

Maria Elena Buemi

Student at Univ de Buenos Aires

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Proceedings Article | 26 April 2007 Paper

Landmine detection using B-spline deformable contours in IR images

Juliana Gambini, María Buemi, Horacio Abbate, Eduardo Castro, Julio Jacobo Berllés, Juan Miguel Santos, Marta Mejail

Proceedings Volume 6553, 655312 (2007) https://doi.org/10.1117/12.719724

KEYWORDS: Land mines, Infrared imaging, Statistical analysis, Image segmentation, Detection and tracking algorithms, Infrared detectors, Thermography, Infrared radiation, Infrared cameras, Solar radiation

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Presently, the number of landmines planted around the world totalizes more than 110 million and, far from slowing down, the landmine production planting rate is, at least, one order of magnitude higher than the rate at which they are removed. In this work a technique to detect buried landmines using boundary detection in IR images, is presented. The buried objects have different temperature than the surrounding soil. We find the object contours by means of an algorithm of B-Spline deformable curves. Under a statistical model, regions with different temperatures can be characterized by the values of the statistical parameters of these distributions. Therefore, this information can be used to find boundaries among different regions in the image. The B-Spline approach has been widely used in curve representation for boundary detection, shape approximation, object tracking and contour detection. Contours formulated by means of B-Splines allow local control, require few parameters and are intrinsically smooth. The algorithm consists in estimating the parameters along lines strategically disposed on the image. The true boundary is found when the values of these parameters vary abruptly on both sides. A likelihood function is maximized to determine the position of such boundaries. We present the experimental results, which show the behavior of the detection method, according to the buried object depth and the elapsed time from the cooling initial time. The obtained results exhibit that it is possible to recognize the shape of the objects, buried at different depths, with a low computational effort.

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