基于灰度直方图的运动目标特征检测算法
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摘要
为提高运动目标的检测效果和指导性,提出一种基于灰度直方图分析的运动目标特征检测算法。采用视觉成像技术进行运动目标图像采集和视觉特征分析,提取运动目标的动态视觉特征量。根据运动目标边缘差分变换和空间位置关系进行运动图像的特征分离,提取运动目标图像的边缘轮廓特征量。采用统计形状模型进行运动目标图像的二值化分离,构建运动目标图像的灰度直方图。根据灰度直方图中的统计信息进行目标特征检测和动态特征提取,实现运动目标图像的视觉检测和动态识别,有效提取运动目标的关键特征,实现目标特征检测。仿真结果表明,采用该方法进行运动目标图像的特征检测性能较好,对运动目标的动态识别能力较强。
In order to improve the detection effect and guidance of moving targets, a feature detection algorithm based on gray histogram analysis is proposed. The moving object image is collected and the visual feature is analyzed by using the visual imaging technology, and the dynamic visual feature quantity of the moving object is extracted. The feature separation of the moving image is carried out according to the moving object edge differential transform and the spatial position relation, and the edge contour feature of moving object image is extracted. The binary separation of moving object image is carried out by using statistical shape model, and the gray histogram of moving object image is constructed. According to the statistical information of gray histogram, the object feature detection and dynamic feature extraction are carried out to realize the visual detection and dynamic recognition of moving target image. The key features of moving object are extracted effectively and the target feature detection is realized. The simulation results show that the proposed method has better performance in feature detection of moving target images and dynamic recognition ability for moving targets.
In order to improve the detection effect and guidance of moving targets, a feature detection algorithm based on gray histogram analysis is proposed. The moving object image is collected and the visual feature is analyzed by using the visual imaging technology, and the dynamic visual feature quantity of the moving object is extracted. The feature separation of the moving image is carried out according to the moving object edge differential transform and the spatial position relation, and the edge contour feature of moving object image is extracted. The binary separation of moving object image is carried out by using statistical shape model, and the gray histogram of moving object image is constructed. According to the statistical information of gray histogram, the object feature detection and dynamic feature extraction are carried out to realize the visual detection and dynamic recognition of moving target image. The key features of moving object are extracted effectively and the target feature detection is realized. The simulation results show that the proposed method has better performance in feature detection of moving target images and dynamic recognition ability for moving targets.
引文
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[3] 张宇,王晓燕.基于混合灰度差指标的低空目标图像检测方法[J].电子测量与仪器学报,2015,29(8):1196-1202.
[4] 单玉刚,汪家宝.鲁棒的自适应尺度和方向的目标跟踪方法[J].计算机工程与应用,2018,54(21):208-216.
[5] 林兆华,米阳,葛兵.一种粗精结合的快速图像自动调焦算法[J].国外电子测量技术,2015,34(10):28-31.
[6] 王小玉,张亚洲,陈德运.基于多块局部二值模式特征和人眼定位的人脸检测[J].仪器仪表学报,2014,35(12):2739-2745.
[7] 王鑫,周韵,宁晨,等.自适应融合局部和全局稀疏表示的图像显著性检测[J].计算机应用,2018,38(3):866-872.
[8] CHENG M M,MITRA N J,HUANG X L,et al.Global contrast based salient region detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):569-582.
[9] LIU N,HAN J W.DHSNet:Deep hierarchical saliency network for salient object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.2016:678-686.
[10] KIM W,KIM C.Spatiotemporal saliency detection using textural contrast and its applications[J].IEEE Transactions on Circuits & Systems for Video Technology,2014,24(4):646-659.
[11] YAN Q,XU L,SHI J P,et al.Hierarchical saliency detection[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition.2013:1155-1162.
[12] WANG X,NING C,XU L Z.Spatiotemporal saliency model for small moving object detection in infrared videos[J].Infrared Physics & Technology,2015,69:111-117.
[13] 李知娴.激光三维运动成像技术研究[J].激光杂志,2018,39(10):119-123.
[14] WEI X S,LUO J H,WU J X.Selective convolutional descriptor aggregation for fine-grained image retrieval[J].IEEE Transactions on Image Processing,2017,26(6):2868-2881.
[15] RADENOVIC F,TOLIAS G,CHUM O.CNN image retrieval learns from BoW:Unsupervised fine-tuning with hard examples[C]// Proceedings of 2016 European Conference on Computer Vision.2016:3-20.
[16] BACH S H,BROECHELER M,HUANG B,et al.Hinge-loss Markov random fields and probabilistic soft logic[J].Journal of Machine Learning Research,2017,18(109):1-67.
[17] FARNADI G,BACH S H,MOENS M F,et al.Soft quantification in statistical relational learning[J].Machine Learning,2017,106(12):1971-1991.
[18] RAMES A,RODRIGUEZ M,GETOOR L.Multi-relational influence models for online professional networks[C]// Proceedings of 2017 International Conference on Web Intelligence.2017:291-298.
[19] ARBABI A,HORIE Y,BALL A J,et al.Subwavelength-thick lenses with high numerical apertures and large efficiency basedon high-contrast transmitarrays[J].Nature Communications,2015,6(5):7069-7078.
[20] EHSAN A,AMIR A,SEYEDEH M K,et al.Multiwavelength polarization-insensitive lenses based on dielectric metasurfaces with meta-molecules[J].Optics,2016,3(6):628-633.
[21] OSKOOI A F,ROUNDY D,IBANESCU M,et al.Meep:A flexible free-software package for electromagnetic simulations by the FDTD method[J].Computer Physics Communications,2010,181(3):687-702.
[22] HU A Z,ZHANG R,YIN D,et al.Image quality assessment using a SVD-based structural projection[J].Image Communication,2014,29(3):293-302.
[23] SLIMENI F,SCHEERS B,NIR V L,et al.Learning multi-channel power allocation against smart jammer in cognitive radio networks[C]// Proceedings of 2016 International Conference on Military Communications and Information Systems.2016:1-7.