A two-level approach for modeling and fusion of antipersonnel mine detection sensors in terms of belief functions within the Dempster-Shafer framework is presented. Three promising and complementary sensors are considered: a metal detector, an infrared camera, and a ground-penetrating radar. Since the metal detector, the most often used mine detection sensor, provides measures that have different behaviors depending on the metal content of the observed object, the first level aims at identifying this content and at providing a classification into three classes. Depending on the metal content, the object is further analyzed at the second level toward deciding the final object identity. This process can be applied to any problem where one piece of information induces different reasoning schemes depending on its value. A way to include influence of various factors on sensors in the model is also presented, as well as a possibility that not all sensors refer to the same object. An original decision rule adapted to this type of application is proposed, as well as a way for estimating confidence degrees. More generally, this decision rule can be used in any situation where the different types of errors do not have the same importance. Some examples of obtained results are shown on synthetic data mimicking reality and with increasing complexity. Finally, applications on real data show promising results. include 3-D image and object processing, 3D and fuzzy mathematical morphology, discrete 3-D geometry and topology, decision theory, information fusion in image processing, fuzzy set theory, evidence theory, structural pattern recognition, spatial reasoning, and medical imaging.
In this paper, chamcterization of mine detection sensors in terms of belief functions and their fusion are presented. Need f o r fusion of mine detection sensors is discussed, and reasons for choosing Dempster-Shafer framework are pointed out, taking into account specificity and sensitivity of the problem. This work is done i n the scope of the HUDEM' project, where three promising and complementary sensors are under analysis. These sensors are presented, and detail analysis i s performed i n case of fusing the data f " them. A way for including in the model influence of various factors on sensors and their results i s discussed as well and will be further analyzed in the future. The application of the approach proposed in this paper is illustmted on the frequent case of detecting metallic objects, but the possibility for modifying it to some other situations ezists.
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