The object recognition problem when features fail to be homogeneous

Abstract The goal of the present work is to obtain a reasonable solution to the problem of object identification. Sensors report on certain independent feature values of an object. The Dempster-Shafer theory is used to integrate the information coming from these independent sources. Moreover, the sensors do not report the feature values in a crisp manner. These values are only stochastically determined. Also, in the data base itself, objects only partially belong to classes determined by feature views. This might be due to the inability of the expert or expert system to pinpoint exactly the feature value of a given object. This setting naturally leads to applying the Dempster-Shafer theory to masses whose focal elements are fuzzy sets. A similar approach is taken to produce an economical solution to the problem of object identification. A set of sensors is picked based on performance evaluation.

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