公开(公告)号：US10049299B2

公开(公告)日：2018-08-14

申请号：US15439896

申请日：2017-02-22

申请人： BEIHANG UNIVERSITY

发明人： Xiaowu Chen ,  Kan Guo ,  Dongqing Zou ,  Qinping Zhao

IPC分类号： G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

摘要： The invention discloses a deep learning based method for three dimensional (3D) model triangular facet feature learning and classifying and an apparatus. The method includes: constructing a deep convolutional neural network (CNN) feature learning model; training the deep CNN feature learning model; extracting a feature from, and constructing a feature vector for, a 3D model triangular facet having no class label, and reconstructing a feature in the constructed feature vector using a bag-of-words algorithm; determining an output feature corresponding to the 3D model triangular facet having no class label according to the trained deep CNN feature learning model and an initial feature corresponding to the 3D model triangular facet having no class label; and performing classification. The method enhances the capability to describe 3D model triangular facets, thereby ensuring the accuracy of 3D model triangular facet feature learning and classifying results.

公开(公告)号：US09384781B2

公开(公告)日：2016-07-05

申请号：US14332813

申请日：2014-07-16

申请人： BEIHANG UNIVERSITY

发明人： Xiaowu Chen ,  Dongqing Zou ,  Qinping Zhao ,  Jianwei Li

IPC分类号： G11B27/00 ,  H04N5/93 ,  G11B27/02 ,  H04N1/60 ,  G06T5/00 ,  G06T11/00

CPC分类号： G11B27/02 ,  G06T5/001 ,  G06T11/001 ,  G06T2207/10024 ,  H04N1/60

摘要： This invention provides a method of sparse representation of contents of high-resolution video images which supports content editing and propagation. It mainly comprises five steps which are: (1) to input the original images or videos of high resolution to summarize the characteristic information of their pixels; (2) to acquire the highly sparse samples of the original images or videos through the sparse representation technique; (3) to reconstruct each pixel of the input images or videos with a small number of the original sparse samples to calculate a coefficient of reconstruction; (4) to edit and propagate the original sparse samples to yield a result of new sparse samples; (5) to generate a result of final images or videos of high resolution according to the result of sparse samples and the coefficient of reconstruction. This invention only edits and propagates the highly sparse samples rather than all the information of pixels. Thus, the memory consumption can be greatly reduced so as to possibly process the images or videos of high resolution in a very small memory space. It is very potential to be widely applied in the fields of image processing, computer vision and augmented reality technique.

摘要翻译： 本发明提供了一种支持内容编辑和传播的高分辨率视频图像内容的稀疏表示方法。 它主要包括五个步骤：（1）输入高分辨率的原始图像或视频，以总结其像素的特征信息; （2）通过稀疏表示技术获取原始图像或视频的高度稀疏样本; （3）用少量的原始稀疏样本重建输入图像或视频的每个像素，以计算重建系数; （4）编辑和传播原始稀疏样本以产生新的稀疏样本的结果; （5）根据稀疏样本和重建系数的结果生成最终图像或高分辨率视频的结果。 本发明仅编辑和传播高度稀疏的样本，而不是像素的全部信息。 因此，可以大大减少存储器消耗，以便可能在非常小的存储空间中处理高分辨率的图像或视频。 在图像处理，计算机视觉和增强现实技术领域中广泛应用是非常有潜力的。

公开(公告)号：US11974050B2

公开(公告)日：2024-04-30

申请号：US17865986

申请日：2022-07-15

申请人： Beihang University

发明人： Jia Li ,  Daxin Gu ,  Yunshan Qi ,  Qinping Zhao

IPC分类号： H04N5/232 ,  H04N5/355 ,  H04N23/80 ,  H04N25/57

CPC分类号： H04N23/80 ,  H04N25/57

摘要： The embodiments of the present disclosure disclose data simulation method and device for event camera. A specific embodiment of the method includes: decoding the video to be processed to obtain a video frame sequence; inputting a target video frame to a fully convolutional network UNet to obtain event camera contrast threshold distribution information; sampling each pixel in the target video frame to obtain an event camera contrast threshold set; performing processing on the event camera contrast threshold set and the video frame sequence, to obtain the simulated event camera data; performing generative adversarial learning on the simulated event camera data and event camera shooting data, to obtain updated event camera contrast threshold distribution information; generating simulated event camera data. The present disclosure is a computer vision system that can be widely applied to such fields as national defense and military, film and television production, public security, and etc.

公开(公告)号：US11461999B2

公开(公告)日：2022-10-04

申请号：US17061400

申请日：2020-10-01

申请人： Beihang University

发明人： Jia Li ,  Kui Fu ,  Kai Mu ,  Qinping Zhao

IPC分类号： G06V10/48 ,  G06V10/25 ,  G06V10/426

摘要： The embodiments of this disclosure disclose an image object detection method, device, electronic equipment, and computer-readable medium. A specific mode of carrying out the method includes: performing region segmentation on a target image to obtain at least one image region; performing feature extraction on each image region in the at least one image region to obtain at least one feature map; generating a semantic relation graph and a spatial distribution relation graph based on the at least one feature map and the at least one image region; generating an image region relation graph based on the semantic relation graph and spatial distribution relation graph; determining a target image region from the at least one image region based on the image region relation graph; displaying the target image region. This implementation mode achieves an improvement of user experience and a growth of network traffic.

公开(公告)号：US20210034845A1

公开(公告)日：2021-02-04

申请号：US16729293

申请日：2019-12-27

申请人： BEIHANG UNIVERSITY

发明人： Feng Lu ,  Qinping Zhao

IPC分类号： G06K9/00 ,  G06Q10/06

摘要： This disclosure provides a method and an apparatus for monitoring a working state, which automatically collect an image of a staff in real time, determines point of gaze information of the staff based on the image of the staff thus collected, and further determines the working state of the staff according to the point of gaze information. Since the whole process does not require the participation of the staff, the normal work of the staff is not disturbed. Moreover, the accuracy in the monitoring of the working state is improved by avoiding influence of the subjective factors on the assessment result if staff participation is involved.

公开(公告)号：US10672130B2

公开(公告)日：2020-06-02

申请号：US15892347

申请日：2018-02-08

申请人： BEIHANG UNIVERSITY

发明人： Xiaowu Chen ,  Dongqing Zou ,  Xiaogang Wang ,  Zongji Wang ,  Qinping Zhao

IPC分类号： G06T7/174 ,  G06T7/11 ,  G06T7/162

摘要： Disclosed is a co-segmentation method and apparatus for a three-dimensional model set, which includes: obtaining a super patch set for the three-dimensional model set which includes at least two three-dimensional models, each of the three-dimensional models including at least two super patches; obtaining a consistent affinity propagation model according to a first predefined condition and a conventional affinity propagation model, the consistent affinity propagation model being constraint by the first predefined condition which is position information for at least two super patches that are in the super patch set and belong to a common three-dimensional model set; converting the consistent affinity propagation model into a consistent convergence affinity propagation model; clustering the super patch set through the consistent convergence affinity propagation model to generate a co-segmentation outcome for the three-dimensional model set. The disclosed three-dimensional model set co-segmentation method and apparatus improves consistency between three-dimensional model set co-segmentation outcomes.

公开(公告)号：US10152797B2

公开(公告)日：2018-12-11

申请号：US15448540

申请日：2017-03-02

申请人： BEIHANG UNIVERSITY

发明人： Xiaowu Chen ,  Kan Guo ,  Qinping Zhao

IPC分类号： G06K9/34 ,  G06T7/11 ,  G06T7/60 ,  G06T17/00

摘要： Presently disclosed is a method for co-segmenting three-dimensional models represented by sparse and low-rank feature, comprising: pre-segmenting each three-dimensional model of a three-dimensional model class to obtain three-dimensional model patches for the each three-dimensional model; constructing a histogram for the three-dimensional model patches of the each three-dimensional model to obtain a patch feature vector for the each three-dimensional model; performing a sparse and low-rank representation to the patch feature vector for the each three-dimensional model to obtain a representation coefficient and a representation error of the each three-dimensional model; determining a confident representation coefficient for the each three-dimensional model according to the representation coefficient and the representation error of the each three-dimensional model; and clustering the confident representation coefficient of the each three-dimensional model to co-segment the each three-dimensional model respectively.

公开(公告)号：US10082868B2

公开(公告)日：2018-09-25

申请号：US15413165

申请日：2017-01-23

申请人： BEIHANG UNIVERSITY

发明人： Feng Lu ,  Xiaowu Chen ,  Qinping Zhao

IPC分类号： G06K9/00 ,  G06F3/01

CPC分类号： G06F3/013 ,  G06K9/00201 ,  G06K9/00228 ,  G06K9/00604 ,  G06K9/0061

摘要： The invention provides a calculation method of line-of-sight direction based on analysis and match of iris contour in human eye image, including: a data driven method, for stable calculation of 3D line-of-sight direction via inputting human eye image to be matched with synthetic data of virtual eyeball appearance; two novel optimization matching criterions of eyeball appearance, which effectively reduce effects of uncontrollable factors, such as image scaling and noise on results; a joint optimization method, for the case of continuously shooting multiple human eye images, to further improve calculation accuracy. One application of the invention is virtual reality and human computer interaction which is under the principle that shooting eye images of a user and calculating line-of-sight direction of user to enable interaction with intelligent system interface or virtual realistic object. The invention can be widely used in training, games and entertainment, video surveillance, medical care and other fields.

公开(公告)号：US09924144B2

公开(公告)日：2018-03-20

申请号：US15007062

申请日：2016-01-26

申请人： BEIHANG UNIVERSITY

发明人： Qinping Zhao ,  Zhong Zhou ,  Tao Yu ,  Chang Xing

IPC分类号： H04N9/31 ,  G03B21/14

CPC分类号： H04N9/3155 ,  G03B15/05 ,  G03B21/14 ,  G03B2215/0567 ,  G03B2215/0592 ,  G06T15/00 ,  H04N9/317 ,  H04N9/3185 ,  H04N9/3194 ,  H05B37/0227

摘要： The invention provides a light field illuminating method, device and system. The method includes: determining, based on position and angle of rays emitted from a projector and focal length of a lens, position and angle of projection rays obtained after the emitted rays being transmitted through a lens array; determining, based on position and angle of projection rays and light probe array of sampled scene, brightness value of projection rays; converting, based on brightness transfer function of projector, brightness value of projection rays into pixel value of projection input image, generating projection input image based on pixel value of projected input image; and performing light field illumination on target object with projection input image. A projector and a lens array are adopted to achieve light field illumination, so that pixel-level accurate lighting control can be achieved, and various complicated light field environments can be simulated vividly in practical scenario.

公开(公告)号：US09613424B2

公开(公告)日：2017-04-04

申请号：US14990470

申请日：2016-01-07

申请人： BEIHANG UNIVERSITY

发明人： Xiaowu Chen ,  Qiang Fu ,  Qinping Zhao ,  Bin Zhou ,  Kan Guo

IPC分类号： G06T17/00 ,  G06T7/00 ,  G06K9/00 ,  G06K9/46 ,  G06K9/52 ,  G06T19/00 ,  G06T7/73 ,  G06T7/50

CPC分类号： G06T7/50 ,  G06K9/00369 ,  G06K9/4604 ,  G06K9/52 ,  G06T7/75 ,  G06T17/00 ,  G06T19/00 ,  G06T2207/20092 ,  G06T2207/30124 ,  G06T2210/16

摘要： A method of constructing 3D clothing model based on single image, estimating a 3D model of human body of an inputted image and constructing 3D clothing plane according to the clothing silhouette of the inputted image. The method includes utilizing the 3D clothing plane and the 3D model of human body to generate a smooth 3D clothing model through a deformation algorithm. A decomposition algorithm of intrinsic image is utilized along with a shape-from-shading algorithm to acquire a set of detail information of clothing from the inputted image. A weighted Laplace editing algorithm is utilized to shift the acquired detail information of clothing to the smooth 3D clothing model to yield a final 3D clothing model. A 3D clothing model is used to generate the surface geometry details including folds, wrinkles.