基于智能学习的海量红外激光图像特征挖掘技术
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摘要
传统基于FPGA的快速图像特征提取方法,未对图像实施轮廓构建,导致特征挖掘结果不理想,提出基于智能学习的海量红外激光图像特征挖掘方法。构建红外激光图像的活动轮廓模型,对图像实施小波降噪处理,对降噪后的海量红外激光图像进行活动轮廓线套索融合检索,基于检索结果采用SIFT算法实现海量红外激光图像特征挖掘。实验结果表明,所设计方法进行海量红外激光图像降噪的误差小于1%,特征挖掘平均用时约为8. 63 s,特征挖掘准确率高达98%以上,所设计方法能够用于海量红外激光图像特征的准确、高效挖掘。
The traditional fast image feature extraction method based on FPGA does not construct the contour of the image,which leads to the unsatisfactory result of feature mining. The active contour model of infrared laser image is constructed,and the image is denoised by wavelet transform. The active contour of the denoised infrared laser image is fused and retrieved. Based on the retrieved results,the SIFT algorithm is used to mine the features of the massive infrared laser image. The experimental results show that the error of denoising is less than 1%,the average time of feature mining is about 8. 63 seconds,and the accuracy of feature mining is over 98%. The method can be used for accurate and efficient feature mining of massive infrared laser images.
The traditional fast image feature extraction method based on FPGA does not construct the contour of the image,which leads to the unsatisfactory result of feature mining. The active contour model of infrared laser image is constructed,and the image is denoised by wavelet transform. The active contour of the denoised infrared laser image is fused and retrieved. Based on the retrieved results,the SIFT algorithm is used to mine the features of the massive infrared laser image. The experimental results show that the error of denoising is less than 1%,the average time of feature mining is about 8. 63 seconds,and the accuracy of feature mining is over 98%. The method can be used for accurate and efficient feature mining of massive infrared laser images.
引文
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[4]刘兴旺,王江晴,徐科.一种融合Auto Encoder与CNN的混合算法用于图像特征提取[J].计算机应用研究,2017,34(12):3839-3843.
[5]秦煜,吴静静,安伟.基于RANSAC的激光网格标记图像特征提取[J].计算机工程与科学,2017,39(8):1495-1501.
[6] LU X,GU D,WANG Y,et al. Feature Extraction of Welding Seam Image Based on Laser Vision[J]. IEEE Sensors Journal,2018,18(11):4715-4724.
[7]郭慧玲,廖利,杨俊.随机加密图像中高效特定图像定位方法仿真[J].计算机仿真,2015,32(3):434-437.
[8]刘子腾,白瑞林,王秀平.基于激光视觉的角焊缝图像特征点提取[J].焊接学报,2016,37(2):89-93.
[9]赵磊,朱永利,贾亚飞,等.基于GLCM和LBP的局部放电灰度图像特征提取[J].电测与仪表,2017,54(1):77-82.
[10] LIU S,HU Jianhua,ZHANG R,et al. Development of Mining Technology and Equipment for Seafloor Massive Sulfide Deposits[J]. Chinese Journal of Mechanical Engineering,2016,29(5):863-870.
[11]王明超,李盈,杨舒羽.基于视觉感知特性的红外图像质量评价方法[J].激光与红外,2017,47(7):921-924.
[12]王博,万磊,李晔,等.基于自适应脉冲耦合神经网络的水下激光图像分割方法[J].光学学报,2015,35(4):119-128.
[13]黄鸿,何凯,郑新磊,等.基于深度学习的高光谱图像空-谱联合特征提取[J].激光与光电子学进展,2017,54(10):174-182.
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[15] YEH C H,LEE G,LIN C Y. Robust Laser Speckle Authentication System Through Data Mining Techniques[J].IEEE Transactions on Industrial Informatics,2017,11(2):505-512.
[16]张合新,王强,张腾飞,等.激光主动成像图像边缘检测算法研究[J].电光与控制,2015,46(4):1192-1196.
[17]彭晨,余柏蒗,吴宾,等.基于移动激光扫描点云特征图像和SVM的建筑物立面半自动提取方法[J].地球信息科学学报,2016,18(7):878-885.