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361.发明授权公开(公告)号：US11893685B2
公开(公告)日：2024-02-06
申请号：US17755172
申请日：2021-11-17
Inventor： Junjun Zhang , Yi Zeng , Xiaopei Hou
IPC: G06T17/05 , G06V10/764 , G06V10/46 , G06T3/40 , G06T5/50
CPC classification number: G06T17/05 , G06T3/40 , G06T5/50 , G06V10/46 , G06V10/764 , G06T2207/20221
Abstract: The present disclosure provides a landform map building method and apparatus, an electronic device and a readable storage medium, and relates to the field of image processing technologies. The method for building landform map includes: acquiring a to-be-processed image to obtain a grayscale image of the to-be-processed image; classifying pixels in the grayscale image according to grayscale values to obtain binary images corresponding to different landform categories; extracting image spot contours of image spots in the binary images, and taking the extracted image spot contours as vector graphs to obtain a vector graph set; merging, according to position information, the vector graphs corresponding to a same landform category in the vector graph set, and obtaining a first landform map according to merging results corresponding to different landform categories; and mapping, by using a preset landform type, the vector graphs corresponding to different landform categories in the first landform map, and taking a mapping result as a second landform map.
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公开(公告)号：US11893081B2
公开(公告)日：2024-02-06
申请号：US17485776
申请日：2021-09-27
IPC: G06V10/00 , G06F18/2137 , G06V10/48 , G06T15/20
CPC classification number: G06F18/21375 , G06T15/205 , G06V10/48
Abstract: This application discloses a map display method and apparatus, where the method includes: obtaining an initial image, where a first road is included in the initial image. The initial image is divided into a first sub-image and a second sub-image, where the first road is not included in the first sub-image, and the first road is included in the second sub-image. The first sub-image is mapped to a first plane of a transition model, and the second sub-image is mapped to a second plane of the transition model, where there is an included angle between the first plane and the second plane, and the first plane is parallel to a screen of a display device. The second plane is controlled to move by means of the transition model, until the included angle is reduced from a first angle to a second angle, and a three-dimensional map display animation is obtained.
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公开(公告)号：US20240037911A1
公开(公告)日：2024-02-01
申请号：US18109522
申请日：2023-02-14
Inventor： Ying Xin , Song Xue , Yuan Feng , Chao Li , Bin Zhang , Yunhao Wang , Shumin Han
IPC: G06V10/764 , G06V10/44 , G06V10/80 , G06V10/82
CPC classification number: G06V10/764 , G06V10/44 , G06V10/806 , G06V10/82
Abstract: Provided is an image classification method, an electronic device and a storage medium, relating to a field of artificial intelligence technology, and specifically, to the technical fields of deep learning, image processing and computer vision, which may be applied to scenes such as image classification. The image classification method includes: extracting a first image feature of a target image by using a first network model, where the first network model includes a convolutional neural network module; extracting a second image feature of the target image by using a second network model, where the second network model includes a deep self-attention transformer network (Transformer) module; fusing the first image feature and the second image feature to obtain a target feature to be recognized; and classifying the target image based on the target feature to be recognized.
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公开(公告)号：US11887473B2
公开(公告)日：2024-01-30
申请号：US17698347
申请日：2022-03-18
Inventor： Xiongfei Tan , Yiming Zhang , Tingting Ge , Xun Gan
CPC classification number: G08G1/0141 , G06V20/54 , G08G1/04
Abstract: A road congestion detection method realized by a computer includes obtaining K images taken by L cameras corresponding to a first road at a same target time point, the first road including M road sections, each road section corresponding to at least one camera, M, L, K each being a positive integer, L being greater than or equal to M, performing target detection on each image to obtain area ratio information, the area ratio information representing a ratio of an area of vehicles on a lane of a target road section to an area of the lane of the target road section, the target road section being a road section corresponding to the image in the M road sections, and determining a first congestion detection result of the first road at the target time point in accordance with K pieces of area ratio information corresponding to the M road sections.
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365.发明公开公开(公告)号：US20240013558A1
公开(公告)日：2024-01-11
申请号：US18113266
申请日：2023-02-23
Inventor： Haoran WANG , Dongliang HE , Fu LI , Errui DING
IPC: G06V20/70 , G06V10/774 , G06V20/40 , G06F40/30 , G06F40/279
CPC classification number: G06V20/70 , G06V10/774 , G06V20/46 , G06F40/30 , G06F40/279
Abstract: There is provided cross-modal feature extraction, retrieval, and model training methods and apparatuses, and a medium, which relates to the field of artificial intelligence (AI) technologies, and specifically to fields of deep learning, image processing, and computer vision technologies. A specific implementation solution involves: acquiring to-be-processed data, the to-be-processed data corresponding to at least two types of first modalities; determining first data of a second modality in the to-be-processed data, the second modality being any of the types of the first modalities; performing semantic entity extraction on the first data to obtain semantic entities; and acquiring semantic coding features of the first data based on the first data and the semantic entities and by using a pre-trained cross-modal feature extraction model.
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Patent Agency Ranking
公开(公告)号：US20240013364A1
公开(公告)日：2024-01-11
申请号：US18003992
申请日：2022-01-29
Inventor： Xinyi DAI , Wei ZHANG , Xiaolin LIANG
IPC: G06T7/00
CPC classification number: G06T7/0002 , G06T2200/24 , G06T2207/20084 , G06T2207/20104
Abstract: An image-based vehicle damage assessment method includes: obtaining an image to be assessed of a damaged vehicle; outputting a first damage assessment box and first damage information based on the image to be assessed; in response to a first box selection operation on the image to be assessed, determining and outputting a second damage assessment box and second damage information corresponding to the first box selection operation, in which a vehicle damage image indicated by the second damage assessment box is different from the vehicle damage image indicated by the first damage assessment box, and the second damage information is vehicle damage information of the vehicle damage image indicated by the second damage assessment box.
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公开(公告)号：US20230419610A1
公开(公告)日：2023-12-28
申请号：US18185359
申请日：2023-03-16
Inventor： Xing LIU , Ruizhi CHEN , Yan ZHANG , Chen ZHAO , Hao SUN , Jingtuo LIU , Errui DING , Tian WU , Haifeng WANG
CPC classification number: G06T17/20 , G06T5/50 , G06V10/26 , G06V10/60 , G06T2207/10028 , G06T2207/20221
Abstract: An image rendering method includes the steps below. A model of an environmental object is rendered to obtain an image of the environmental object in a target perspective. An image of a target object in the target perspective and a model of the target object are determined according to a neural radiance field of the target object. The image of the target object is fused and rendered into the image of the environmental object according to the model of the target object.
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公开(公告)号：US20230419601A1
公开(公告)日：2023-12-28
申请号：US17989996
申请日：2022-11-18
Inventor： Sha Li , Yaqian Shen , Zhidong Yin , Zhixiang Chen
IPC: G06T17/00
CPC classification number: G06T17/00 , G06T2215/16 , G06T2210/12
Abstract: Provided is a method for adjusting a target object, an electronic device, and a storage medium, relating to a field of computer technology, and in particular, to fields of intelligent transportation, automatic driving, computer vision, and the like. The method includes: acquiring a first bounding box corresponding to the target object and a second bounding box corresponding to a support of the target object; obtaining an association relationship for adjustment of the target object, according to a spatial position where the first and second bounding boxes are located; and moving, according to the association relationship, the target object towards the support, to obtain a third bounding box corresponding to the adjusted target object, where the third bounding box and the second bounding box appear to fit each other.
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公开(公告)号：US20230401828A1
公开(公告)日：2023-12-14
申请号：US17905965
申请日：2022-04-08
Inventor： Meina QIAO , Shanshan LIU , Xiameng QIN , Chengquan ZHANG , Kun YAO
IPC: G06V10/774 , G06V30/14 , G06V10/764
CPC classification number: G06V10/774 , G06V10/764 , G06V30/1444
Abstract: A method for training an image recognition model includes: obtaining a training data set, in which the training data set includes first text images of each vertical category in a non-target scene and second text images of each vertical category in a target scene, and a type of text content involved in the first text images is the same as a type of text content involved in the second text image; training an initial recognition model by using the first text images, to obtain a basic recognition model; and modifying the basic recognition model by using the second text images, to obtain an image recognition model corresponding to the target scene.
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公开(公告)号：US20230401221A1
公开(公告)日：2023-12-14
申请号：US18063509
申请日：2022-12-08
Inventor： Lu GAN , Zenghui XU , Zhiqun XIA , Jianbing ZHANG , Lianghui CHEN , Jian GONG , Ke SUN
IPC: G06F16/2458 , G06F16/242 , G06F16/2453
CPC classification number: G06F16/2458 , G06F16/2433 , G06F16/24542
Abstract: Provided are a cross-tables search method, an electronic device and a storage medium, relating to a field of artificial intelligence, and in particular, to natural language processing, big data, knowledge graph technology, which may be applied in scenarios of smart cloud, smart city, and smart government affairs. The cross-tables search method includes: parsing a query to obtain an entity, attribute and relationship required to be searched; determining a cross-tables search strategy according to the entity, the attribute and the relationship; and performing a cross-tables search operation according to the cross-tables search strategy.
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