检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:郭小燕[1] 陈鹏 张明[3] 张绿云[2] 马楚奇 GUO Xiao-yan;CHEN Peng;ZHANG Ming;ZHANG Lyu-yun;MA Chu-qi(School of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China;College of Big Data and Computer Science,Hechi University,Yizhou 546300,China;School of Information Engineering,Lanzhou City University,Lanzhou 730070,China)
机构地区:[1]甘肃农业大学信息科学技术学院,甘肃兰州730070 [2]河池学院大数据与计算机学院,广西宜州546300 [3]兰州城市学院信息工程学院,甘肃兰州730070
出 处:《软件导刊》2023年第4期162-167,共6页Software Guide
基 金:甘肃农业大学盛彤苼基金项目(GSAU-STS-2021-16);甘肃农业大学青年导师基金项目(QAU-QDFC-2021-18);2021年广西高校中青年教师科研基础能力提升项目(2021KY0619);河池学院2020年校级自然科学一般项目(RZ2100000751)。
摘 要:为降低图像噪声及PCA_CNN网络计算量,将彩色图像采用灰度化处理,利用Canny算子进行边缘处理,采用大津算法、轮廓提取算法完成银行卡数字区域识别,使该模型对图像的背景、光照、对比度有较强适应能力与抗干扰性。利用主成分分析法(PCA)选取卷积神经网络(CNN)模型卷积核,从而避免大量迭代造成时间及算力浪费,对Sig⁃moid激活函数进行改进使其分段单调递增,从而提高识别准确率。改进后的PCA_CNN模型识别率为98.53%,与CNN模型、传统BP神经网络、SVM模型、Bytes模型、暹罗网络模型相比,在准确率与收敛速度方面均有一定优势。实验结果表明,改进后的PCA_CNN模型可以从手机等非专业摄影设备在自然光下拍摄的银行卡照片中有效提取银行卡号。The color image is grayed to reduce the image noise and the PCA_CNN network calculation.The Canny operator is used for edge processing,and the OTSU algorithm and contour extraction algorithm are used to complete the bank card digital area recognition,so that the recognition task has strong adaptability and anti-interference to the background,illumination and contrast of the image.Therefore,principal component analysis(PCA)is used to select the convolution kernel of convolutional neural network(CNN)model to avoid time and computa⁃tional waste caused by a large number of iterations.The sigmoid activation function is improved to make it piecewise monotonically increasing,so as to improve the accuracy of recognition.The recognition rate of the improved PCA_CNN model is 98.53%,which has certain advantages in accuracy and convergence speed compared with CNN method,traditional BP neural network,SVM model,Bytes model and Siam network model.The experimental results show that the PCA_CNN model can effectively extract the bank card number from the bank card photos taken by non-professional photographic equipment such as mobile phones under natural light.
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:13.59.141.195