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Patent Agency Ranking
公开(公告)号：US20170278220A1
公开(公告)日：2017-09-28
申请号：US15126271
申请日：2016-03-17
Inventor： Lerenhan LI , Nong SANG , Changxin GAO , Luxin YAN , Jin WANG , Shiwei ZHANG , Yuanjie SHAO , Juncai PENG
CPC classification number: G06T5/002 , G06T3/4007 , G06T3/4076 , G06T7/0002 , G06T2207/10048 , G06T2207/20076
Abstract: The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.
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公开(公告)号：US20210192251A1
公开(公告)日：2021-06-24
申请号：US16079565
申请日：2018-01-08
Inventor： Nong SANG , Lerenhan LI , Hao YAN , Changxin GAO , Luxin YAN , Shiwei ZHANG , Zhixiong PI
Abstract: The present invention discloses a method and system for selecting an image region that facilitates blur kernel estimation, in which the method includes: calculating a relative total variation value of each pixel in a blurred image to obtain a relative total variation mapping image; setting a threshold value to determine whether respective pixel in the image is a boundary pixel or not; then sampling the blurred image and its relative total variation mapping image to obtain a series of image patches; and finally counting the number of boundary pixels in each mapping image patch and selecting out an image region that facilitates blur kernel estimation. According to the method and the system, the problems of excessive dependency on operator experience, low efficiency and the like in the existing region selection methods are effectively solved. The image region that facilitates blur kernel estimation is automatically selected out. And the method and the system are suitable for the application occasion of the blur kernel estimation in an image deblurring algorithm.
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公开(公告)号：US20210365743A1
公开(公告)日：2021-11-25
申请号：US17253124
申请日：2020-06-23
Inventor： Nong SANG , Chuchu HAN , Yuanjie SHAO , Ruochen ZHENG , Changxin GAO
Abstract: The invention discloses a person search method based on person re-identification driven localization refinement. On one hand, the region of interest (ROI) conversion module converts an original input image into a small image corresponding to a ROI, and contradiction existing in part of features shared by a person re-identification network and a detection network is avoided; and on the other hand, loss of the person re-identification network can be transmitted back to the detection network in a gradient manner through the ROI conversion module, the supervision of loss of the person re-identification network for the detection bounding box output by the detection network is realized, and the adjusted detection bounding box can effectively remove background interference, contains more useful attribute information and is more suitable for person search, so that the person search accuracy is greatly improved.
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公开(公告)号：US20210248378A1
公开(公告)日：2021-08-12
申请号：US16965015
申请日：2020-01-07
Inventor： Nong SANG , Shiwei ZHANG , Zhiyuan LI , Changxin GAO , Yuanjie SHAO
Abstract: A spatiotemporal action detection method includes performing object detection on all frames of a sample video to obtain a candidate object set; calculating all interframe optical flow information on the sample video to obtain a motion set; constructing a spatiotemporal convolution-deconvolution network of an attention mechanism and a motion attention mechanism of an additional object; adding both a corresponding sparse variable and a sparse constraint to obtain a network structure S after performing spatiotemporal convolution processing on each time segment of the sample video; training the network structure S with an objective function based on classification loss and loss of the sparse constraint of cross entropy; and calculating an action category and a sparse coefficient corresponding to each time segment of a test sampled video to obtain an object action spatiotemporal location.
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公开(公告)号：US20180068430A1
公开(公告)日：2018-03-08
申请号：US15709454
申请日：2017-09-19
Inventor： Nong SANG , Lerenhan LI , Luxin YAN , Changxin GAO , Yuanjie SHAO , Juncai PENG , Shiwei ZHANG , Jin WANG
CPC classification number: G06T7/0002 , G06F17/11 , G06F17/18 , G06K9/40 , G06K9/4628 , G06N3/02 , G06T5/003 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084
Abstract: A method for estimating a blur kernel size, the method including: (1) pre-processing a blurred image, to obtain an image, so that a size of the image satisfies an image input size of a multi-class convolutional neural network (CNN); (2) inputting the image into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector; and (3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image is the blur kernel size corresponding to a largest element. The invention also provides a system for estimating a blur kernel size. The system includes an image pre-processing module, a training-set synthesizing module, a multi-class CNN module, and a blur kernel size estimation module.
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