Tsinghua University

Boe Technology Group Co., Ltd.

Fuzhou Boe Optoelectronics Technology Co., Ltd.

Carestream Health, Inc.

Ge Medical Systems Global Technology Company, LLC

Beijing Dajia Internet Information Technology Co., Ltd.

Beihang University

Beijing Horizon Robotics Technology Research and Devlopment Co., Ltd.

Xinyu Zhang

Jun Li

Zhiwei Li

Huaping Liu

Li Wang

Xingang Wu

Hui Chen

Qiaoni Wang

Zhenhong Zou

Chunmei Yang

Xingming Chen

Zhijian Chen

Xin Xie

Yabin Lin

Minggang She

Qifan Tan

Yuchao Liu

Ken Sun

Mingzhi Chen

Jianxi Luo

Lijun Zhao

Lei Zhu

Yi Huang

Jing Jing Zhang

Lei Yang

Tianlei Zhang

Bin Ye

Mo Zhou

Chun Hai Chen

Yunpeng Wang

Jun Liu

Haiyang Yu

Shijian Luo

Yilong Ren

Kangyao Huang

Shiyan Zhang

Hanwen Gao

Rentao Sun

Mona Zhang

Yiqian Lu

Shichun Guo

Wenju Gao

A long-length imaging method based on digital radiography, a digital radiography device, an image processing system and a computer-readable memory medium to implement the method. The long-length imaging method comprises the following steps: A. determining a part of interest of an anatomy of an object; B. determining imaging parameters of digital radiography based on the part of interest, the imaging parameters at least including a plurality of specified positions of imaging components, wherein the plurality of specified positions are configured to capture X-ray images including the part of interest of the anatomy of the object; and C. generating a long-length image in relation to the part of interest of the anatomy of the object based on the captured X-ray images.

Long length imaging method and device based on digital radiography

A method for establishing co-hosting in live-streaming is disclosed. The method includes: displaying a co-host mode setting portal in a live-streaming room of a first user account; displaying a first co-host mode, a second co-host mode, a third co-host mode in response to a trigger operation on the co-host mode setting portal, and the second user account is a viewer account in the live-streaming room; and determining a selected co-host mode as a target co-host mode for the live-streaming room in response to a select operation on any one of the first co-host mode, the second co-host mode, and the third co-host mode.

Method for establishing co-hosting in live-streaming, and terminal

A vision-LiDAR fusion method and system based on deep canonical correlation analysis are provided. The method comprises: collecting RGB images and point cloud data of a road surface synchronously; extracting features of the RGB images to obtain RGB features; performing coordinate system conversion and rasterization on the point cloud data in turn, and then extracting features to obtain point cloud features; inputting point cloud features and RGB features into a pre-established and well-trained fusion model at the same time, to output feature-enhanced fused point cloud features, wherein the fusion model fuses RGB features to point cloud features by using correlation analysis and in combination with a deep neural network; and inputting the fused point cloud features into a pre-established object detection network to achieve object detection. A similarity calculation matrix is utilized to fuse two different modal features.

Vision-LiDAR fusion method and system based on deep canonical correlation analysis

A takeoff and landing control method of a multimodal air-ground amphibious vehicle includes: receiving dynamic parameters of the multimodal air-ground amphibious vehicle; processing the dynamic parameters by a coupled dynamic model of the multimodal air-ground amphibious vehicle to obtain dynamic control parameters of the multimodal air-ground amphibious vehicle, wherein the coupled dynamic model of the multimodal air-ground amphibious vehicle comprises a motion equation of the multimodal air-ground amphibious vehicle in a touchdown state; and the motion equation of the multimodal air-ground amphibious vehicle in a touchdown state is determined by a two-degree-of-freedom suspension dynamic equation and a six-degree-of-freedom motion equation of the multimodal air-ground amphibious vehicle in the touchdown state; and controlling takeoff and landing of the multimodal air-ground amphibious vehicle according to the dynamic control parameters of the multimodal air-ground amphibious vehicle. The method is used for takeoff and landing control of a multimodal air-ground amphibious vehicle.

Takeoff and landing control method and control apparatus of multimodal air-ground amphibious vehicle, and computer storage medium

An X-ray image quality control system includes an image acquisition unit configured to acquire an X-ray image and determine the type of imaged body part and the type of projection mode. A quality detection unit is configured to detect an image defect in X-ray image with regard to the type of the imaged body part and/or the type of the projection mode. The quality control system effectively identifies image defects including inaccurate positioning, patient movement, external object artifacts, and poor exposure. The system provides feedback to the technician at the image acquisition point to adjust the image acquisition solution in a timely manner.

X-ray image quality control system

A 3D object detection method based on multi-view feature fusion of 4D RaDAR and LiDAR point clouds includes simultaneously acquiring RaDAR point cloud data and LiDAR point cloud data; and inputting the RaDAR point cloud data and the lidar point cloud data into a pre-established and trained RaDAR and LiDAR fusion network and outputting a 3D object detection result, wherein the RaDAR and LiDAR fusion network is configured to learn interaction information of a LiDAR and a RaDAR from a bird's eye view and a perspective view, respectively, and concatenate the interaction information to achieve fusion of the RaDAR point cloud data and the lidar point cloud data. The method can combine advantages of RaDAR and LiDAR, while avoiding disadvantages of the two modalities as much as possible to obtain a better 3D object detection result.

3D object detection method based on multi-view feature fusion of 4D RaDAR and LiDAR point clouds

A target detection method based on the fusion of prior positioning of a millimeter-wave radar and a visual feature includes: simultaneously obtaining, based on the millimeter-wave radar and a vehicle-mounted camera after being calibrated, point cloud data of the millimeter-wave radar and a camera image; performing spatial 3D coordinate transformation on the point cloud data to project transformed point cloud data onto a camera plane; generating a plurality of anchor samples based on projected point cloud data according to a preset anchor strategy, and obtaining a final anchor sample based on a velocity-distance weight of each candidate region; fusing RGB information of the camera image and intensity information of an RCS in the point cloud data to obtain a feature of the final sample; and inputting the feature of the final anchor sample into a detection network to generate category and position information of a target in a scenario.

Target detection method based on fusion of prior positioning of millimeter-wave radar and visual feature

An all-weather target detection method based on a vision and millimeter wave fusion includes: simultaneously acquiring continuous image data and point cloud data using two types of sensors of a vehicle-mounted camera and a millimeter wave radar; pre-processing the image data and point cloud data; fusing the pre-processed image data and point cloud data by using a pre-established fusion model, and outputting a fused feature map; and inputting the fused feature map into a YOLOv5 detection network for detection, and outputting a target detection result by non-maximum suppression. The method fully fuses millimeter wave radar echo intensity and distance information with the vehicle-mounted camera images. It analyzes different features of a millimeter wave radar point cloud and fuses the features with image information by using different feature extraction structures and ways, so that the advantages of the two types of sensor data complement each other.

All-weather target detection method based on vision and millimeter wave fusion

A point cloud intensity completion method and system based on semantic segmentation are provided. The point cloud intensity completion method includes: acquiring an RGB image and point cloud data of a road surface synchronously by a photographic camera and a lidar; performing spatial transformation on the point cloud data by using a conversion matrix to generate a two-dimensional reflection intensity projection map and a two-dimensional depth projection map; performing reflection intensity completion on the RGB image and the two-dimensional reflection intensity projection map to obtain a single-channel reflection intensity projection map; performing depth completion on the RGB image and the two-dimensional depth projection map to obtain a single-channel depth projection map; and performing coarse-grained completion on the RGB image, the single-channel reflection intensity projection map and the single-channel depth projection map to obtain a two-dimensional coarse-grained reflectance intensity projection map.

Point cloud intensity completion method and system based on semantic segmentation

An automatic calibration method and system for spatial positions of a laser radar and a camera sensor is provided. The method includes: adjusting a spatial position of the laser radar relative to the camera sensor to obtain a plurality of spatial position relationships of the laser radar and the camera sensor; for a spatial position relationship, calculating a gray value of each laser radar point conforming to the line features after projection as a score, and accumulating scores of all laser radar points as a total score; traversing all the spatial position relationships to obtain a plurality of total scores; and selecting a spatial position relationship of the laser radar and the camera sensor corresponding to a highest total score from the plurality of total scores to serve as a calibrated position relationship of the laser radar and the camera sensor.

Automatic calibration method and system for spatial position of laser radar and camera sensor

A lane line detection method using a fusion network based on an attention mechanism, and a terminal device are provided. The method includes: synchronously acquiring natural images and point cloud data of a road surface; and inputting the natural images and the point cloud data into a pre-built and trained fusion network to output a lane line detection result. Time series frames and an attention mechanism are added to the fusion network to perform information fusing on the point cloud data and the natural images. Specifically, continuous frames are used to improve detection network performance to deal with complex situations such as label loss and vehicle being blocked; low-dimensional features are stitched with high-dimensional features by Skip Connection to make up for image detail information that is continuously lost as the network goes deeper, and the Decoder module is used to restore image dimensions to obtain a final result.

Detection method using fusion network based on attention mechanism, and terminal device

An adaptive multi-sensor data fusion method based on mutual information includes: receiving an RGB image of a road surface collected by a camera; receiving point cloud data of the road surface collected synchronously by LIDAR; preprocessing the point cloud data to obtain dense point cloud data; and inputting the RGB image and the dense point cloud data into a pre-established and well-trained fusion network, to output data fusion results. The fusion network is configured to calculate mutual information of a feature tensor and an expected feature of input data, assign fusion weights of the input data according to the mutual information, and then output the data fusion results according to the fusion weights. In the new method, such information theory tool as mutual information is introduced, to calculate the correlation between the extracted feature of the input data and the expected feature of the fusion network.

Adaptive multi-sensor data fusion method and system based on mutual information

Disclosed are a method and an apparatus for controlling storage operations of data of Region of Interest (ROI). The method may include obtaining data of a first ROI, determining to keep the data of the first ROI according to a score of the first ROI, and overriding data of a second ROI already stored in a storage region for storing ROI data with the data of the first ROI in a case where a remaining capacity of the storage region is insufficient. Related electric device and non-transitory computer readable medium are also disclosed.

Method and apparatus for controlling storage operations of data of region of interest

A deep multimodal cross-layer intersecting fusion method, a terminal device and a storage medium are provided. The method includes: acquiring an RGB image and point cloud data containing lane lines, and pre-processing the RGB image and point cloud data; and inputting the pre-processed RGB image and point cloud data into a pre-constructed and trained semantic segmentation model, and outputting an image segmentation result. The semantic segmentation model is configured to implement cross-layer intersecting fusion of the RGB image and point cloud data. In the new method, a feature of a current layer of a current modality is fused with features of all subsequent layers of another modality, such that not only can similar or proximate features be fused, but also dissimilar or non-proximate features can be fused, thereby achieving full and comprehensive fusion of features. All fusion connections are controlled by a learnable parameter.

Deep multimodal cross-layer intersecting fusion method, terminal device, and storage medium

A LiDAR point cloud reflection intensity complementation method includes: acquiring a grayscale image and an original point cloud of a same road surface using a calibrated vehicle-mounted webcam and LiDAR; extracting edge information of the grayscale image using a preset edge extraction strategy, to obtain an edge image of the grayscale image; preprocessing the original point cloud to obtain an original point cloud reflection intensity projection image and an interpolated complementary point cloud reflection intensity projection image; and inputting the grayscale image, the edge image of the grayscale image, the original point cloud reflection intensity projection image and the interpolated complementary point cloud reflection intensity projection image into a pre-trained point cloud reflection intensity complementation model, and outputting a complementary point cloud reflection intensity projection image. A LiDAR point cloud reflection intensity complementation system is further provided.

LiDAR point cloud reflection intensity complementation method and system

Growing community of inventors

Beijing, China

Xinyu Zhang