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1.发明授权公开(公告)号：US10168141B2
公开(公告)日：2019-01-01
申请号：US15358828
申请日：2016-11-22
Inventor： Yunping Chen , Ling Tong , Weihong Han , Wenhuan Wang , Chuanqi Zhong , Jiaming Liang , Jia Huang
Abstract: Techniques for identifying air pollution sources based on aerosol retrieval and a glowworm swarm algorithm are described herein. According to these techniques, a satellite remote sensing image is obtained and a wind speed vector is obtained from the satellite remote sensing image. A GSO algorithm is applied, introducing a glowworm similarity correction factor obtained from an attribute value and a wind speed and wind direction correction factor obtained from the wind speed vector, and pollution sources are identified.
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公开(公告)号：US09401008B2
公开(公告)日：2016-07-26
申请号：US14598512
申请日：2015-01-16
Inventor： Yunping Chen , Ling Tong , Yan Chen , Bo Gao , Yu Tian , Yuxia Li , Weihong Han , Yang Li
CPC classification number: G06T5/00 , G06K9/4647 , G06T7/13 , G06T7/73 , G06T2207/20192
Abstract: A method for identifying and extracting a linear object from an image is disclosed. The method comprises: acquiring an original image to be processed, wherein the original image is taken by a camera, received through network transmission or copied from a compact disc or a removable disk; preprocessing the original image to obtain an enhanced image; extracting an edge information image from the enhanced image; then extracting linear features by performing, on the edge information image, a linear feature extracting transform improved with a cluster operator; finally; identifying and extracting the linear object by distinguishing the linear object from other linear features by considering characteristics of the linear object to be identified and extracted. According to the invention, a linear feature extracting transform improved with a cluster operator is constructed from a distribution of edge pixels in the edge information image along a 2-dimensional direction, which makes it possible to extract, rapidly and accurately, weak linear objects such as power lines from images having complicated background and sub-pixels.
Abstract translation: 公开了一种从图像中识别和提取线性对象的方法。 该方法包括：获取要处理的原始图像，其中原始图像由相机拍摄，通过网络传输接收或从光盘或可移动盘复制; 预处理原始图像以获得增强图像; 从增强图像提取边缘信息图像; 然后通过在边缘信息图像上执行利用集群算子改进的线性特征提取变换来提取线性特征; 最后; 通过考虑待识别和提取的线性对象的特征，通过区分线性对象与其他线性特征来识别和提取线性对象。 根据本发明，通过沿着二维方向的边缘信息图像中的边缘像素的分布来构建利用集群算子改进的线性特征提取变换，这使得可以快速和准确地提取弱线性对象 作为具有复杂背景和子像素的图像的电力线。
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Patent Agency Ranking
公开(公告)号：US20200327673A1
公开(公告)日：2020-10-15
申请号：US16699119
申请日：2019-11-29
Inventor： Ling Tong , Fanghong Xiao
Abstract: A method for thinning and connection in linear object extraction from an image and including the following steps: 1. extracting direction features using various sliding windows from the binary image obtained of linear objects. 2. decomposing the binary image into several binary image layers according to the direction features. 3. extracting thinned curves and endpoints of each binary image layer by conducting curve fitting on each connected component using coordinate information. 4. connecting the thinned curves by computing spatial distances between the endpoints belonging to different thinned curves, angles between the tangential direction and connected direction vectors of the connected points. Finally, the road network image is constructed by overlaying image layers with the thinned curves.
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公开(公告)号：US20220390495A1
公开(公告)日：2022-12-08
申请号：US17467328
申请日：2021-09-06
IPC: G01R23/16
Abstract: The present invention is related to a signal spectrum analysis technology based on linear frequency modulation transformation (LFM) and fast digital pulse compression, which comprises two parts: a circuit for linear frequency modulation signal and an algorithm for fast digital pulse compression. Wherein, in the circuit the modulated chirp signals are obtained by the input signals mixing with the LO chirp signal and then filtered by the band-pass filter the intermediate frequency (IF) chirp signals are produced. The IF signals are composed of the chirp signals with the same frequency band and the chirp rate, but different initial times. Due to the IF chirp signals being orthogonal to each other, the spectrum of the input signals is extracted by the initial time and the orthogonal accumulation. The full spectrum of the input signal is obtained by changing the start position of the sampling data sets along the time axis. The present invention achieves fast high-resolution spectrum analysis by combining the circuit for linear frequency modulation signal and the algorithm for fast digital pulse compression.
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公开(公告)号：US10055828B2
公开(公告)日：2018-08-21
申请号：US15361933
申请日：2016-11-28
Inventor： Yunping Chen , Yaju Xiong , Ling Tong , Weihong Han
IPC: G06T7/00 , G06K9/46 , H04N5/33 , G06K9/62 , H04N17/00 , G06T7/11 , G06K9/00 , G06K9/34 , G06K9/52
CPC classification number: G06T7/0002 , G06K9/0063 , G06K9/34 , G06K9/4642 , G06K9/4661 , G06K9/52 , G06K9/6212 , G06K9/6267 , G06T7/11 , G06T7/143 , G06T2207/10036 , G06T2207/30181 , G06T2207/30204 , H04N5/33 , H04N17/002
Abstract: The present invention discloses a method for retrieving atmospheric aerosol based on statistical segmentation. Firstly a multi-band remote sensing image including an apparent reflectance and an aerosol optical thickness look-up table corresponding to a retrieval band is obtained, then pixels are partitioned and screened according to apparent reflectance segments of a mid-infrared 2.1 micrometer band. After that the retained pixel sets are further partitioned and screened according to the apparent reflectance segments of the mid-infrared 1.6 micrometer band. Finally the obtained pixel sets are partitioned into two categories according to the pixel number, one category including pixels having more pixels, the other including those with less pixels.The category with more pixels is taken as the reference part for retrieval. Specifically, the pixel sets are first searched for the clean segment, then the ground surface reflectivity of the clean segment is taken as the ground surface reflectivity of the whole pixel set, thereby obtaining the aerosol thickness value through retrieval. After that these pixels are taken as references to perform retrieval on the other category.The present invention can improve accuracy and resolution of the retrieval result of the bright ground surface area, and is applied to a wider range.
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公开(公告)号：US09734398B2
公开(公告)日：2017-08-15
申请号：US14958369
申请日：2015-12-03
Inventor： Yunping Chen , Ling Tong , Weihong Han
CPC classification number: G06K9/00637 , G06K9/00651 , G06K9/3241 , G06K9/4604 , G06K9/4633 , G06T7/13 , G06T2207/10032 , G06T2207/30184
Abstract: A method and apparatus for identifying an object are disclosed. The method includes: performing linear feature detection on an image to be identified by using a linear feature detecting method to obtain detected linear features, wherein the linear feature detection method transforms detection of linear features in an image space to detection of extremal points in another space and assigns larger weights to continuous image points than to discrete image points during the transformation by using a continuous cluster factor; and identifying an object to be identified from the detected linear features by considering characteristics of the object to be identified. The method and apparatus for identifying an object of the invention, when used to detect and identify weak linear objects in high resolution remote sensing images, can effectively suppress the system noise and ambient noise, thereby successfully identifying the interested object and avoiding false alarms. Moreover, short line segments can also be identified.
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公开(公告)号：US11042986B2
公开(公告)日：2021-06-22
申请号：US16699119
申请日：2019-11-29
Inventor： Ling Tong , Fanghong Xiao
Abstract: A method for thinning and connection in linear object extraction from an image and including the following steps: 1. extracting direction features using various sliding windows from the binary image obtained of linear objects. 2. decomposing the binary image into several binary image layers according to the direction features. 3. extracting thinned curves and endpoints of each binary image layer by conducting curve fitting on each connected component using coordinate information. 4. connecting the thinned curves by computing spatial distances between the endpoints belonging to different thinned curves, angles between the tangential direction and connected direction vectors of the connected points. Finally, the road network image is constructed by overlaying image layers with the thinned curves.
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