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公开(公告)号：US20240362923A1
公开(公告)日：2024-10-31
申请号：US18628702
申请日：2024-04-06
发明人： Suting XU , Maximilian SCHAEFER , Kun ZHAO
CPC分类号： G06V20/58 , G06T7/246 , G06V10/803 , G06V10/82 , G06T2207/20084 , G06T2207/30261
摘要： A method is provided for predicting respective trajectories of a plurality of road users. Trajectory characteristics of the road users are determined with respect to a host vehicle via a perception system, wherein the trajectory characteristics are provided as a joint vector describing respective dynamics of each of the road users for a predefined number of time steps. The joint vector of the trajectory characteristics is encoded via an algorithm which included an attention algorithm for modelling interactions of the road users. The encoded trajectory characteristics and encoded static environment data obtained for the host vehicle are fused in order to provide fused encoded features. The fused encoded features are decoded in order to predict the respective trajectory of each of the road users for a predetermined number of future time steps.
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公开(公告)号：US20240362828A1
公开(公告)日：2024-10-31
申请号：US18565927
申请日：2022-04-24
发明人： Miao HUA , Bingchuan LI
CPC分类号： G06T11/00 , G06T3/40 , G06T7/73 , G06V10/774 , G06V10/80 , G06V40/193 , G06T2207/20081 , G06T2207/30201
摘要： The present disclosure relates to a video generation method and apparatus, a device, and a storage medium. After acquiring one or more images to be processed, a plurality of target images having different degrees of eye opening are generated according to the images to be processed, and a video having a process of gradual eye change is generated on the basis of the plurality of target images
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3.发明公开公开(公告)号：US20240362804A1
公开(公告)日：2024-10-31
申请号：US18638919
申请日：2024-04-18
发明人： Shuhei OGAWA
CPC分类号： G06T7/50 , G06T5/70 , G06V10/44 , G06V10/806 , G06T2207/20084
摘要： An image processing apparatus is provided. The apparatus acquires input data including a captured image and/or information relating to the captured image. The apparatus acquires a feature of the input data by performing processing on the input data using a neural network. The apparatus generates an integrated feature by integrating the feature and at least some of the input data. The apparatus generates an estimation result of at least one of a defocus range and a depth range for a subject within the captured image, by performing processing on the integrated feature.
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公开(公告)号：US20240362791A1
公开(公告)日：2024-10-31
申请号：US18307353
申请日：2023-04-26
申请人： Adobe Inc.
发明人： Yuqian Zhou , Chuong Huynh , Connelly Barnes , Elya Shechtman , Sohrab Amirghodsi , Zhe Lin
IPC分类号： G06T7/12 , G06F3/04883 , G06V10/44 , G06V10/74 , G06V10/80
CPC分类号： G06T7/12 , G06F3/04883 , G06V10/44 , G06V10/761 , G06V10/806 , G06T2207/20101 , G06V2201/07
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning to generate a mask for an object portrayed in a digital image. For example, in some embodiments, the disclosed systems utilize a neural network to generate an image feature representation from the digital image. The disclosed systems can receive a selection input identifying one or more pixels corresponding to the object. In addition, in some implementations, the disclosed systems generate a modified feature representation by integrating the selection input into the image feature representation. Moreover, in one or more embodiments, the disclosed systems utilize an additional neural network to generate a plurality of masking proposals for the object from the modified feature representation. Furthermore, in some embodiments, the disclosed systems utilize a further neural network to generate the mask for the object from the modified feature representation and/or the masking proposals.
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公开(公告)号：US12131583B2
公开(公告)日：2024-10-29
申请号：US17949880
申请日：2022-09-21
申请人： Apple Inc.
发明人： Hendrik Dahlkamp , Vinay Sharma , Nitin Gupta , Floris Chabert , Andrew C. Edwards , Mitchell Williams , Jonghoon Jin , Nicholas M. Fraioli , Aravindhan K. Krishnan , Patrick L. Coffman
IPC分类号： G06K9/36 , G06F18/22 , G06F21/60 , G06T7/11 , G06V10/80 , G06V20/52 , G06V40/16 , G06V40/50 , H04L9/08
CPC分类号： G06V40/173 , G06F18/22 , G06F21/602 , G06T7/11 , G06V10/803 , G06V20/53 , G06V40/172 , G06V40/50 , H04L9/085 , G06T2207/20132 , G06T2207/30201
摘要： Techniques are disclosed for providing a notification that a person is at a particular location. For example, a resident device may receive from a user device an image that shows a face of a first person, the image being captured by a first camera of the user device. The resident device may also receive, from another device having a second camera, a second image showing a portion of a face of a second person, the second camera having a viewable area showing a particular location. The resident device may determine a score indicating a level of similarity between a first set of characteristics associated with the face of the first person and a second set of characteristics associated with the face of a second person. The resident device may then provide to the user device a notification based on determining the score.
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公开(公告)号：US12118803B2
公开(公告)日：2024-10-15
申请号：US17564524
申请日：2021-12-29
CPC分类号： G06V20/588 , G06V10/75 , G06V10/762 , G06V10/806 , G06V10/82 , G06V10/95 , H04L12/40 , H04L2012/40273
摘要： A method of road detection based on Internet of Vehicles is provided, the method is applied to vehicle terminals and includes: obtaining a target road image captured by an image collection terminal and inputting it into an improved YOLOv3 network, performing feature extraction by using backbone network of dense connection to obtain feature images with different scales; performing feature fusion of top-to-down and dense connection to the feature images by using an improved feature pyramid networks (FPN) to obtain prediction results; obtaining attribute information of the target road image according to the prediction results; the attribute information includes positions and categories of objects in the target road image; the improved YOLOv3 is formed by based on YOLOv3 network, replacing residual modules of backbone network to dense connection modules, increasing feature extraction scale, optimizing feature fusion mode of FPN, performing pruning and performing network recovery processing guided by knowledge distillation.
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公开(公告)号：US12118772B2
公开(公告)日：2024-10-15
申请号：US17687664
申请日：2022-03-06
发明人： Jixi Gao , Wenming Shen , Mingyong Cai , Xinsheng Zhang , Wenfei Tai , Xuewei Shi , Xuhui Chen , Sihan Liu , Tong Xiao , Wandong Ma , Jing Li , Yuanli Shi , Lixia Wang , Hongwei Zhang , Zhihua Ren
IPC分类号： G06V20/52 , G06V10/762 , G06V10/80 , G06V10/82 , G06V40/20
CPC分类号： G06V10/803 , G06V10/762 , G06V10/806 , G06V10/82 , G06V20/52 , G06V40/20
摘要： A human activity recognition fusion method and system for ecological protection red line is disclosed. The method includes: obtaining a pre-stage remote sensing image and a post-stage remote sensing image of a target ecological protection red line region, and performing a data pre-processing; inputting the pre-processed pre-stage remote sensing image and the post-stage remote sensing image into a human activity recognition model after a pre-training; identifying a human activity pattern of the target ecological protection red line region as a first detection result; segmenting, calculating and analyzing the latest image data corresponding to the target ecological protection red line region based on a geographical country situation data to obtain a change pattern as a second detection result; and fusing the first detection result and the second detection result to obtain a change detection pattern of the target ecological protection red line region.
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公开(公告)号：US12112554B2
公开(公告)日：2024-10-08
申请号：US18366529
申请日：2023-08-07
CPC分类号： G06V20/588 , B60W30/0956 , B60W40/06 , B60W50/0097 , G01C21/3815 , G06V10/80 , G06V20/58
摘要： In one aspect, the present disclosure is directed at a computer implemented method for determining a drivable area in front of a host vehicle. According to the method, a region of interest is monitored in front of the host vehicle by at least two sensors of a detection system of the host vehicle. The region of interest is divided into a plurality of areas via a computer system of the host vehicle, and each area of the plurality of areas is classified as drivable area, non-drivable area or unknown area via the computer system based on fused data received by the at least two sensors.
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公开(公告)号：US12112519B1
公开(公告)日：2024-10-08
申请号：US17678090
申请日：2022-02-23
申请人： Apple Inc.
IPC分类号： G06V10/74 , G06T17/00 , G06V10/762 , G06V10/80 , G06V20/70
CPC分类号： G06V10/761 , G06T17/00 , G06V10/7635 , G06V10/80 , G06V20/70 , G06T2210/61
摘要： An exemplary process obtains sensor data for a physical environment, generates a local scene graph for the physical environment based on the sensor data, wherein the local scene graph represents a set of objects and relationships between the objects, matches the local scene graph with a principal scene graph of a set of principal scene graphs, and executes one or more scripted actions involving the objects based on a narrative associated with the matched principal scene graph. In some implementations, the set of principal scene graphs is generated by generating local scene graphs for a plurality of environments, and generating individual scene graphs each representative of local scene graphs.
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10.发明公开公开(公告)号：US20240331416A1
公开(公告)日：2024-10-03
申请号：US18550267
申请日：2022-11-11
发明人： Yulan Hu , Zhenzhong Zhang , Xiaotian Jiang
IPC分类号： G06V20/69 , A61B5/00 , G06T7/00 , G06V10/62 , G06V10/70 , G06V10/77 , G06V10/774 , G06V10/80 , G06V20/50 , G16B40/20
CPC分类号： G06V20/698 , A61B5/0042 , G06T7/0016 , G06V10/62 , G06V10/7715 , G06V10/774 , G06V10/806 , G06V10/87 , G06V20/50 , G16B40/20 , G06T2207/20081 , G06T2207/20084 , G06T2207/30016 , G06T2207/30096 , G06V2201/03
摘要： The present disclosure provides a method of processing medical data, which includes: acquiring first medical image data; inputting the first medical image data into a first feature extraction network to obtain a first image feature; and obtaining a first gene mutation information according to the first image feature. The first feature extraction network includes a first feature extraction module configured to: determine a first image query matrix and a first image key matrix according to the first medical image data; determine a first image weight matrix according to the first image query matrix and the first image key matrix, where the first image weight matrix represents a correlation information between each two first medical images in the first medical image data; and determine the first image feature according to the first image weight matrix and the first medical image data.
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