Two methods for detection of minelike shapes

We discuss the problem of detecting minelike shapes in data coming from mine detection sensors that can provide images, such as an imaging metal detector, an infrared camera or a ground-penetrating radar. Firstly, we show a way for selecting possibly dangerous regions that should be further analyzed, i.e. to which shape analysis methods should be applied. Then, two shape detection methods are presented, both based on the randomized Hough transform. Most of the mines are of a cylindrical shape, so, due to some burial angle, they appear elliptical in 2D images that are taken parallel to the ground. Thus, one of the two presented methods deals with the detection of elliptical shapes. The other method is developed for detecting the hyperbolic signatures of mines in B-scans (vertical data slices into the ground) of ground-penetrating radar data. Finally, pieces of information that can be extracted from detected ellipses and hyperbolas are discussed, and two ways are suggested for their further use towards determining whether a particular selected region contains a mine indeed or not. Both methods are illustrated on real data.