基于光强—光谱—偏振信息融合的水下目标检测

doi:10.3969/j.issn.1000-436x.2013.03.025

Abstract

Abstract:

Difficulties and high computational costs in the model establishmen and parameter estimation seriously de-graded the efficiency of the underwater object detection system, making them too cumbersome to the practical work. A noval light intensity, spectrum and polarization feature fusion method was proposed. This method directly introduces the prior underwater knowledge into the system for feature fusion, getting rid of the harassment of the image preprocessing. Experiments prove that this method by comparison can achieve more reliable results at the lower computational cost.

Key words: underwater image processing, underwater object detection, information fusion, machine learning

Zhe CHEN,Hui-bin WANG,Jie SHEN,Li-zhong XU. Light intensity, spectrum and polarization information fusion based underwater object detection[J]. Journal on Communications, 2013, 34(3): 192-198.

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References 13

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光学先验知识	C_good	C_false
+	0.911 4	0.007 2
-	0.877 0	0.091 9

算法	C_good	C_false	运算时间/s
水下先验知识	0.911 4	0.007 2	1.32
图像恢复	0.915 8	0.196 8	2.87

算法	C_good	C_false	运算时间/s
经验知识	0.894 2	0.026 2	2.52
图像恢复	0.870 2	0.066 9	7.43

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Light intensity, spectrum and polarization information fusion based underwater object detection

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