The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datas...The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.展开更多
虹膜定位是虹膜识别系统中不可或缺的环节,针对传统的虹膜定位方法对镜面反射、眨眼等复杂环境下质量差的虹膜图像定位准确率低、计算复杂度高和鲁棒性差等问题,提出了一种基于改进YOLOv3模型的虹膜快速定位方法。针对眼周图像中虹膜内...虹膜定位是虹膜识别系统中不可或缺的环节,针对传统的虹膜定位方法对镜面反射、眨眼等复杂环境下质量差的虹膜图像定位准确率低、计算复杂度高和鲁棒性差等问题,提出了一种基于改进YOLOv3模型的虹膜快速定位方法。针对眼周图像中虹膜内、外圆尺寸变化不大,将YOLOv3网络的多尺度结构改进为双尺度检测;引入了轻量级网络Mobilev3中bneck块来改进特征提取网络,减小模型复杂度;利用K-means++算法对虹膜数据集进行类聚,获得更优的锚点框;模型边框损失函数采用LossGIoU改进原均方差(mean squared error,MSE)损失函数;利用虹膜特有几何特征,将模型矩形预测框更改为圆形预测框。在CASIA-IrisV4数据集验证表明,改进模型定位准确率为96.32%,平均精度均值(mean average precision,mAP)为99.37%,检测速度为49.4帧/s,模型参数减少到4.13×10^(6)。结果表明改进后的模型较小,并且能够快速精准对虹膜区域定位,具有较高鲁棒性,能够满足虹膜实时定位的场景。展开更多
为探究MADS-box基因家族对德国鸢尾多季花品种花期的影响,本研究基于德国鸢尾(Iris germanica)转录组数据,利用生物信息学方法对MADS-box家族基因进行筛选鉴定,并对其理化性质、功能域及亲/疏水性等方面进行预测分析,且构建德国鸢尾和...为探究MADS-box基因家族对德国鸢尾多季花品种花期的影响,本研究基于德国鸢尾(Iris germanica)转录组数据,利用生物信息学方法对MADS-box家族基因进行筛选鉴定,并对其理化性质、功能域及亲/疏水性等方面进行预测分析,且构建德国鸢尾和模式植物拟南芥MADS-box蛋白家族的系统进化树。结果表明,在德国鸢尾中鉴定出39个MADS-box基因家族成员,其编码的氨基酸序列分子量在4704.58~30112.19 k D之间,理论等电点(pI)介于5.01~10.92之间,其中32个表现为碱性蛋白,7个表现为酸性蛋白。大多数MADS-box蛋白定位于细胞核和线粒体基质,少许分布在其他细胞器均不含有信号肽。α-螺旋和无规则卷曲为二级结构的主要原件,所有蛋白均表现为亲水性蛋白。进化树分析结果显示,德国鸢尾MADS-box蛋白可聚为5大类(MIKC,Mα,Mβ,Mγ,Mδ)。本研究可为今后进一步研究德国鸢尾MADS-box蛋白的生物学功能提供一定的参考依据。展开更多
文摘The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.
文摘虹膜定位是虹膜识别系统中不可或缺的环节,针对传统的虹膜定位方法对镜面反射、眨眼等复杂环境下质量差的虹膜图像定位准确率低、计算复杂度高和鲁棒性差等问题,提出了一种基于改进YOLOv3模型的虹膜快速定位方法。针对眼周图像中虹膜内、外圆尺寸变化不大,将YOLOv3网络的多尺度结构改进为双尺度检测;引入了轻量级网络Mobilev3中bneck块来改进特征提取网络,减小模型复杂度;利用K-means++算法对虹膜数据集进行类聚,获得更优的锚点框;模型边框损失函数采用LossGIoU改进原均方差(mean squared error,MSE)损失函数;利用虹膜特有几何特征,将模型矩形预测框更改为圆形预测框。在CASIA-IrisV4数据集验证表明,改进模型定位准确率为96.32%,平均精度均值(mean average precision,mAP)为99.37%,检测速度为49.4帧/s,模型参数减少到4.13×10^(6)。结果表明改进后的模型较小,并且能够快速精准对虹膜区域定位,具有较高鲁棒性,能够满足虹膜实时定位的场景。
文摘为探究MADS-box基因家族对德国鸢尾多季花品种花期的影响,本研究基于德国鸢尾(Iris germanica)转录组数据,利用生物信息学方法对MADS-box家族基因进行筛选鉴定,并对其理化性质、功能域及亲/疏水性等方面进行预测分析,且构建德国鸢尾和模式植物拟南芥MADS-box蛋白家族的系统进化树。结果表明,在德国鸢尾中鉴定出39个MADS-box基因家族成员,其编码的氨基酸序列分子量在4704.58~30112.19 k D之间,理论等电点(pI)介于5.01~10.92之间,其中32个表现为碱性蛋白,7个表现为酸性蛋白。大多数MADS-box蛋白定位于细胞核和线粒体基质,少许分布在其他细胞器均不含有信号肽。α-螺旋和无规则卷曲为二级结构的主要原件,所有蛋白均表现为亲水性蛋白。进化树分析结果显示,德国鸢尾MADS-box蛋白可聚为5大类(MIKC,Mα,Mβ,Mγ,Mδ)。本研究可为今后进一步研究德国鸢尾MADS-box蛋白的生物学功能提供一定的参考依据。
