Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational compl...Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.展开更多
为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病...为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病变位置的定位与分割。在宫颈病变二分类的研究中,通过多次对比试验后,最终测得宫颈病变分级预测的准确率高达92.95%,与未经改进的方法相比,在时间复杂度与空间复杂度上都有明显的降低。试验表明:文中方法不仅能辅助放射科医生进行病变等级诊断,还可提高诊断的效率与准确率,在临床实践中具有重要意义。展开更多
由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉...由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。展开更多
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.
文摘为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病变位置的定位与分割。在宫颈病变二分类的研究中,通过多次对比试验后,最终测得宫颈病变分级预测的准确率高达92.95%,与未经改进的方法相比,在时间复杂度与空间复杂度上都有明显的降低。试验表明:文中方法不仅能辅助放射科医生进行病变等级诊断,还可提高诊断的效率与准确率,在临床实践中具有重要意义。
文摘由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。
