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XIAOQIAN ZHANG1,* , HANSHAN LI1, JUNCHAI GAO1
In order to improve target tracking stability and target recognition rate, this paper proposes a multi-feature fusion recognition algorithm based on BP neural network and D-S evidence theory, extracts the target features by RGB color weighted histogram and Sobel edge weighted histogram, establishes the decision rule model by combing BP neural network and D-S evidence theory, utilizes the BP neural network to evaluate the reliability of evidence source, according to the characteristic of reliability evaluation, obtains the target multi-feature information fusion decision and recognition of the target by the D-S combination rule. Through experimental comparison, the results show that the target recognition rate is about 95.5% and the misjudgement rate is about 4.5% by using multi-feature information fusion decision under the same conditions, in addition, the recognition results validate that the proposed image multi-feature information fusion decision method can improve effectively the target recognition rate..
Information fusion, BP neural network, D-S evidence theory, Multi-feature fusion decision.
XIAOQIAN ZHANG, HANSHAN LI, JUNCHAI GAO, Research on target recognition method based on multi-feature information fusion decision, Optoelectronics and Advanced Materials - Rapid Communications, 12, 11-12, November-December 2018, pp.634-643 (2018).
Submitted at: July 19, 2018
Accepted at: Nov. 29, 2018
Optoelectronics and Advanced Materials - Rapid Communications
(2023): 0.5 - 2023 Journal Citation Reports (Clarivate Analytics, 2024)
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