多特征判断的合作目标识别方法

doi:10.16182/j.issn1004731x.joss.201806047

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (6): 2377-2383.doi: 10.16182/j.issn1004731x.joss.201806047

多特征判断的合作目标识别方法

孙国鹏¹, 郝向阳¹, 张振杰¹, 闫芃瑞²

1. 信息工程大学,河南郑州 450000;
2. 61287部队,四川成都 610036

收稿日期:2016-04-26 修回日期:2016-08-04 出版日期:2018-06-08 发布日期:2018-06-14
作者简介:孙国鹏(1992-),男,辽宁铁岭,硕士生,研究方向为无人机视觉导航;郝向阳(1966-),男,河南济源,博士生,教授,研究方向为视觉导航与视觉测量。
基金资助:
国家863计划 (2014AA7034057A)

Cooperative Target Recognition with Multiple Features Judgment

Sun Guopeng¹, Hao Xiangyang¹, Zhang Zhenjie¹, Yan Pengrui²

1. Information Engineering University, Zhengzhou 450000, China;
2. 61287 Troops, Chengdu, 610036 China

Received:2016-04-26 Revised:2016-08-04 Online:2018-06-08 Published:2018-06-14

摘要/Abstract

摘要： 针对视觉导航中合作目标识别问题,提出一种多特征递进判断的快速准确识别算法。通过图像轮廓特征、Hu不变矩特征以及FAST角点特征等多特征,对图像进行层层处理和判断,剔除非目标区域,最终实现合作目标的准确识别。在该过程中对算法进行了一些改进和加速策略,以满足实时性要求。室内外实验表明该算法在不同尺度、形变以及环境干扰等情况下依然可以准确地识别出合作目标,有较高的鲁棒性和适应性。

关键词: 合作目标识别, 多特征, 轮廓特征, Hu不变矩, FAST

Abstract: For solving cooperative target recognition problem in visual navigation, a fast and accurate algorithm based on progressive judgment of multiple features is proposed. The image is processed and judged by multiple features including image contours, Hu moment invariants and FAST corners. The non-target areas are eliminated and the real targets are obtained. In the process, algorithm is improved and some accelerated strategies are adopted to meet real-time requirements. Experiments show that the proposed algorithm can identify cooperative target robustly and adaptively with different distances, different angles and environment disturbance.

Key words: cooperation target recognition, multiple features, image contours, Hu moment invariants, FAST

中图分类号:

TP391.41

孙国鹏, 郝向阳, 张振杰, 闫芃瑞. 多特征判断的合作目标识别方法[J]. 系统仿真学报, 2018, 30(6): 2377-2383.

Sun Guopeng, Hao Xiangyang, Zhang Zhenjie, Yan Pengrui. Cooperative Target Recognition with Multiple Features Judgment[J]. Journal of System Simulation, 2018, 30(6): 2377-2383.

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多特征判断的合作目标识别方法

Cooperative Target Recognition with Multiple Features Judgment

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