基于CS的高质量掌静脉图像获取方法
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摘要
为了获得高质量的手掌静脉图像,设计了一种能快速采集到高质量掌静脉图像的多光谱自适应采集系统方法及装置。通过研究高质量静脉图像对应的近红外光强度配比区域,结合图像质量评价模型和布谷鸟搜索(CS)算法搜索策略,构建出760,850,940 nm三种混合近红外光强自适应配比模型。该模型能够快速调优混合光源强度配比,以获得复杂场景下高质量手掌静脉图像。实验结果表明:CS算法模型及系统方案能快速有效地适应不同人群手掌静脉特征,在保证图像质量的前提下提供快速有效的光源配比方案,为在线手掌静脉识别的后续处理提供了良好的基础。
In order to obtain high-quality palm vein image,a multi-spectral adaptive acquisition system method and device that can rapidly acquire high-quality palm vein images is designed. By studying on near-infrared light intensity matching area corresponding to high-quality vein images,combined with image quality assessment model and cuckoo search( CS) algorithm search strategy,three mixed near-infrared intensity adaptive matching models at760 nm,850 nm and 940 nm are constructed. The model can quickly tune the intensity ratio of the hybrid light source to obtain high-quality palm vein images in complex scenes. The experimental results show that the CS algorithm model and system scheme can quickly and effectively adapt to the characteristics of palm veins in different populations,and provide a fast and effective light source matching scheme under the premise of ensuring image quality,which provides a good basis for the follow-up processing of online palm vein recognition.
In order to obtain high-quality palm vein image,a multi-spectral adaptive acquisition system method and device that can rapidly acquire high-quality palm vein images is designed. By studying on near-infrared light intensity matching area corresponding to high-quality vein images,combined with image quality assessment model and cuckoo search( CS) algorithm search strategy,three mixed near-infrared intensity adaptive matching models at760 nm,850 nm and 940 nm are constructed. The model can quickly tune the intensity ratio of the hybrid light source to obtain high-quality palm vein images in complex scenes. The experimental results show that the CS algorithm model and system scheme can quickly and effectively adapt to the characteristics of palm veins in different populations,and provide a fast and effective light source matching scheme under the premise of ensuring image quality,which provides a good basis for the follow-up processing of online palm vein recognition.
引文
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[2]苑玮琦,杨冰.非接触手成像系统图像采集光源的研究[J].激光与红外,2014,44(7):0783-0787.
[3]苑玮琦,杨国天,李威.基于波长选择的手掌静脉采集系统研究[J].激光与红外,2011,41(2):0234-0239.
[4]吴微,苑玮琦,林森,等.手掌静脉识别典型波长选择[J].光学学报,2012,32(12):12110021-12110027.
[5] Wang C,Zeng X,Sun X,et al. Quality assessment on near infraredpalm vein image[D]∥2017 the 32nd Youth Academic AnnualConference(YAC)of Chinese Association of Automation,IEEE,2017:1127-1130.
[6]邓江洪,赵领.基于改进布谷鸟搜索算法的图像边缘检测[J].计算机系统应用,2015,24(8):281-285.
[7]范帅军.布谷鸟搜索算法的应用研究与改进[D].成都:西南交通大学,2016:8-9.
[8]蓝少峰,刘升.布谷鸟搜索算法研究综述[J].计算机工程与设计,2015,36(4):1063-1067.
[9] Wang C,Sun X,Dong W,et al. Quality assessment of palm veinimage using natural scene statistics[C]∥Chinese Conference onComputer Vision,Springer,Singapore,2017:248-255.
[10]苑玮琦,孔德奇.基于多光谱的手掌三模态采集装置[J].仪表技术与传感器,2013(10):0035-0039.
[11]李荣雨,戴睿闻.自适应步长布谷鸟搜索算法[J].计算机科学,2017,44(5):0042-0047.
[12]钟晓雯.非接触多光谱手成像光学系统研制[D].沈阳:沈阳工业大学,2015.
[13]张环,胡德文.手掌静脉识别算法[J].计算机应用,2010,30(S2):0097-0099.
[14]田野,方明.一种改进的布谷鸟搜索算法[J].长春理工大学学报,2017,40(1):0115-0018.