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公开(公告)号：US10803557B2
公开(公告)日：2020-10-13
申请号：US16338703
申请日：2017-12-26
申请人： XIDIAN UNIVERSITY
发明人： Huixin Zhou , Dong Zhao , Runda Qian , Xiuping Jia , Jun Zhou , Hanlin Qin , Bo Yao , Yue Yu , Huan Li , Jiangluqi Song , Bingjian Wang , Yangqun Jin , Shenghui Rong , Kuanhong Cheng , Kun Qian
摘要： A non-uniformity correction method for an infrared image based on guided and high-pass filtering includes: assigning high-frequency component of first image frame of original image sequence to first fixed pattern noise f1; successively loading N-th image frame of the original image sequence with non-uniformity as current image frame, determining a difference between the current image frame and (N−1)-th image frame to obtain (N−1)-th differential image frame, and obtaining a relative change amplitude of each image element of (N−1)-th image frame according to the (N−1)-th differential image frame; and performing high-pass filtering based on a combination of a high-frequency component of the current image frame obtained through guided filtering and the relative change amplitude to obtain n-th fixed pattern noise fn, and performing non-uniformity correction on the current image frame according to fn to obtain a correction result of the current image frame, where N≥2, n≥2.
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公开(公告)号：US11195254B2
公开(公告)日：2021-12-07
申请号：US16980794
申请日：2018-03-28
申请人： XIDIAN UNIVERSITY
发明人： Huixin Zhou , Dong Zhao , Runda Qian , Lixin Guo , Xiuping Jia , Jun Zhou , Maosen Huang , Hanlin Qin , Bo Yao , Yue Yu , Huan Li , Jiangluqi Song , Bingjian Wang , Kuanhong Cheng , Juan Du , Shangzhen Song
摘要： An interframe registration and adaptive step size-based non-uniformity correction method for an infrared image, comprising: first calculating a normalized cross-power spectrum of n-th and (n−1)-th original infrared images with the non-uniformity, and then calculating a horizontal relative displacement and a vertical relative displacement of the n-th and (n−1)-th original infrared images with the non-uniformity; calculating a space variance and a time variance of each pixel of the n-th original infrared image with the non-uniformity, using the obtained space variance and time variance to calculate an adaptive iterative step size of each pixel of the n-th original infrared image with the non-uniformity, and using the iterative step size to update a gain correction coefficient and a bias correction coefficient; finally, performing non-uniformity correction on the pixel in an overlapping area of the n-th and (n−1)-th original infrared images with the non-uniformity.
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公开(公告)号：US11066088B2
公开(公告)日：2021-07-20
申请号：US17270873
申请日：2018-12-13
申请人： XIDIAN UNIVERSITY
发明人： Huixin Zhou , Pei Xiang , Baokai Deng , Yue Yu , Lixin Guo , Dong Zhao , Hanlin Qin , Bingjian Wang , Jiangluqi Song , Huan Li , Bo Yao , Rui Lai , Xiuping Jia , Jun Zhou
IPC分类号： B61L15/00 , B61L27/00 , G06T7/136 , G06T7/11 , G06T7/73 , G06K9/62 , B61K11/00 , B61C17/02 , H04N5/235
摘要： A detection and positioning method for a train water injection port includes: acquiring train water injection port video images, and performing threshold segmentation on the train water injection port video images to obtain binarized train water injection port video images; processing the binarized train water injection port video images and matching the processed binarized train water injection port video images with a train water injection port template image; detecting a position of the water injection port in the train water injection port video image and comparing the position with a pre-set position range where the water injection port is located; and if the position and the pre-set position range have been matched, then transmitting a matching valid signal to a mechanical device control module to control a mechanical device to move or stop and start or stop water injection.
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4.发明申请公开(公告)号：US20200143517A1
公开(公告)日：2020-05-07
申请号：US16338703
申请日：2017-12-26
申请人： XIDIAN UNIVERSITY
发明人： Huixin Zhou , Dong Zhao , Runda Qian , Xiuping Jia , Jun Zhou , Hanlin Qin , Bo Yao , Yue Yu , Huan Li , Jiangluqi Song , Bingjian Wang , Yangqun Jin , Shenghui Rong , Kuanhong Cheng , Kun Qian
摘要： A non-uniformity correction method for an infrared image based on guided and high-pass filtering includes: assigning high-frequency component of first image frame of original image sequence to first fixed pattern noise f1; successively loading N-th image frame of the original image sequence with non-uniformity as current image frame, determining a difference between the current image frame and (N-1)-th image frame to obtain (N-1)-th differential image frame, and obtaining a relative change amplitude of each image element of (N-1)-th image frame according to the (N-1)-th differential image frame; and performing high-pass filtering based on a combination of a high-frequency component of the current image frame obtained through guided filtering and the relative change amplitude to obtain n-th fixed pattern noise fn, and performing non-uniformity correction on the current image frame according to fn to obtain a correction result of the current image frame, where N≥2, n≥2.
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公开(公告)号：US11055574B2
公开(公告)日：2021-07-06
申请号：US17270908
申请日：2018-11-20
申请人： XIDIAN UNIVERSITY
发明人： Huixin Zhou , Jiajia Zhang , Yuanbin Shi , Dong Zhao , Lixin Guo , Hanlin Qin , Bingjian Wang , Rui Lai , Huan Li , Jiangluqi Song , Bo Yao , Yue Yu , Xiuping Jia , Jun Zhou
摘要： A feature fusion and dense connection-based method for infrared plane object detection includes: constructing an infrared image dataset containing an object to be recognized, calibrating a position and class of the object to be recognized in the infrared image dataset, and obtaining an original known label image; dividing the infrared image dataset into a training set and a validation set; performing image enhancement preprocessing on images in the training set, performing feature extraction and feature fusion, and obtaining classification results and bounding boxes through a regression network; calculating a loss function according to the classification results and the bounding boxes in combination with the original known label image, and updating parameter values of a convolutional neural network; repeating the steps to iteratively update the parameters of the convolutional neural network; and processing images in the validation set through the parameters to obtain a final object detection result map.
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