Title :
Mean Deviation Method for Fuzzy Multi-sensor Object Recognition
Author_Institution :
Coll. of Inf. Technol., Jiangxi Univ. of Finance & Economic, Nanchang, China
Abstract :
Aimed at the object recognition problem in which the characteristic values of object types and observations of sensors are in the form of triangular fuzzy numbers, a new fusion method for multi-sensor data is proposed based on mean deviation. The method defines the distance matrix between all object types and unknown object. After solving the optimization problem of maximizing the mean deviations for all attributes, the weights of the attributes are obtained objectively. Thus, the result of recognition for the unknown object is given by the overall distance. The simulated example verifies the feasibility and practicability of the proposed method.
Keywords :
fuzzy set theory; matrix algebra; object recognition; optimisation; sensor fusion; characteristic values; distance matrix; fusion method; fuzzy multisensor object recognition; mean deviation method; multisensor data; object recognition problem; object types; optimization problem; overall distance; triangular fuzzy numbers; Conference management; Databases; Educational institutions; Electronic government; Financial management; Information technology; Object recognition; Sensor fusion; Sensor phenomena and characterization; Technology management; mean deviation; multi-sensor information fusion; object recognition; triangular fuzzy number;
Conference_Titel :
Management of e-Commerce and e-Government, 2009. ICMECG '09. International Conference on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3778-8
DOI :
10.1109/ICMeCG.2009.75
