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公开(公告)号:US12131451B2
公开(公告)日:2024-10-29
申请号:US17872595
申请日:2022-07-25
申请人: Adobe Inc.
IPC分类号: G06T17/20 , G06T3/00 , G06T3/02 , G06T3/14 , G06T3/147 , G06T7/00 , G06T7/70 , G06T17/00 , G06T19/00 , G06T19/20 , G06T1/60 , G06T3/06 , G06T3/067 , G06T7/10
CPC分类号: G06T7/00 , G06T3/00 , G06T3/02 , G06T3/14 , G06T3/147 , G06T7/70 , G06T17/00 , G06T17/20 , G06T17/205 , G06T19/00 , G06T19/20 , G06T1/60 , G06T3/06 , G06T3/067 , G06T7/10 , G06T2200/24 , G06T2207/20021 , G06T2207/20164 , G06T2219/021 , G06T2219/2004 , G06T2219/2016
摘要: In implementations of systems for spatially coherent UV packing, a computing device implements a packing system to identify pairs of boundary vertices of different two-dimensional islands included in a set of two-dimensional islands. A first boundary vertex and a second boundary vertex of the pairs of boundary vertices both correspond to a same three-dimensional coordinate of a three-dimensional mesh. The packing system determines transformations for two-dimensional islands included in the set of two-dimensional islands based on distances between the first boundary vertex and the second boundary vertex of the pairs of boundary vertices. A three-dimensional object is generated for display in a user interface based on the transformations and the three-dimensional mesh.
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2.
公开(公告)号:US12039641B2
公开(公告)日:2024-07-16
申请号:US17722527
申请日:2022-04-18
CPC分类号: G06T11/203 , G06N3/04 , G06T3/147 , G06T11/40
摘要: Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.
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公开(公告)号:US12131435B2
公开(公告)日:2024-10-29
申请号:US17424909
申请日:2020-01-16
IPC分类号: G06K9/00 , G06T3/12 , G06T3/147 , H04N13/111 , H04N13/139
CPC分类号: G06T3/12 , G06T3/147 , H04N13/111 , H04N13/139
摘要: The invention relates to an apparatus for generating or processing an image signal. A first image property pixel structure is a two-dimensional non-rectangular pixel structure representing a surface of a view sphere for the viewpoint. A second image property pixel structure is a two-dimensional rectangular pixel structure and is generated by a processor (305) to have a central region derived from a central region of the first image property pixel structure and at least a first corner region derived from a first border region of the first image property pixel structure. The first border region is a region proximal to one of an upper border and a lower border of the first image property pixel structure. The image signal is generated to include the second image property pixel structure and the image signal may be processed by a receiver to recover the first image property pixel structure.
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公开(公告)号:US12046056B2
公开(公告)日:2024-07-23
申请号:US17379839
申请日:2021-07-19
发明人: Hu Ye , Xiao Han , Kaiwen Xiao , Niyun Zhou , Mingyang Chen
IPC分类号: G16H30/40 , G06F16/51 , G06T3/147 , G06T7/00 , G06V10/94 , G06V20/69 , G16H30/20 , G16H50/70
CPC分类号: G06V20/698 , G06T3/147 , G06T7/0012 , G06V10/945 , G16H30/40
摘要: A computer device obtains a to-be-annotated image having a first magnification. The device obtains an annotated image from an annotated image set, the annotated image distinct from the to-be-annotated image and having a second magnification that is distinct from with the first magnification. The annotated image set includes at least one annotated image. The device matches the to-be-annotated image with the annotated image to obtain an affine transformation matrix, and generates annotation information of the to-be-annotated image according to the affine transformation matrix and the annotated image. In this way, annotations corresponding to images at different magnifications may be migrated. For example, the annotations may be migrated from the low-magnification images to the high-magnification images, thereby reducing the manual annotation amount and avoiding repeated annotations, and further improving annotation efficiency and reducing labor costs.
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公开(公告)号:US12045946B2
公开(公告)日:2024-07-23
申请号:US17448295
申请日:2021-09-21
发明人: Simin Chen , Jie Li , Shi Yao , Biao Yan
CPC分类号: G06T3/147 , G06T3/4038 , G06T7/13 , G06T7/194 , G06T13/80
摘要: A method is provided. The method includes: obtaining a picture to be processed, where the picture to be processed includes a plurality of pixels, and the plurality of pixels comprise first pixels for forming an image and second pixels for forming an image background; rotating the picture to be processed, where for each rotation angle, an intermediate picture is obtained; selecting at least two pictures from the picture to be processed and several intermediate pictures for calculating an area of a bounding box surrounding the image respectively; and removing second pixels outside the bounding box in a picture with the smallest area of bounding box to obtain a processed picture.
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公开(公告)号:US12039690B2
公开(公告)日:2024-07-16
申请号:US17362645
申请日:2021-06-29
发明人: David Justin Rappaport , Russell I. Sanchez , Jonathan L. Worsley , Kae-Ling J. Gurr , Karlton David Powell
摘要: A method for processing a stream of input images is described. A stream of input images that are from an image sensor is received. The stream comprises an initial sequence of input images including a subject having an initial orientation. A change in an angular orientation of the image sensor while receiving the stream of input images is determined. In response to determining the change in the angular orientation, a subsequent sequence of input images of the stream of input images is processed for rotation to counteract the change in the angular orientation of the image sensor and maintain the subject in the initial orientation. The stream of input images is transmitted to one or more display devices.
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公开(公告)号:US12112557B2
公开(公告)日:2024-10-08
申请号:US17390000
申请日:2021-07-30
申请人: ByoungJin Park
发明人: ByoungJin Park
CPC分类号: G06V20/695 , G06T3/147 , G06T3/40 , G06T5/20 , G06T5/92 , G06T7/0012 , G06T2207/10056 , G06T2207/30242
摘要: This application relates to a method for processing image data of a microbial culture medium to recognize colony forming unit (CFU). In one aspect, the method includes receiving, at a processor, captured image data of the microbial culture medium from a user device, and preprocessing, at the processor, the captured image data. The method may also include counting, at the processor, the number of CFUs included in the preprocessed image data to derive result data including the counted number of CFUs. The method may further include automatically inputting information included in the result data into a predetermined template to generate document data corresponding to the captured image data, and transmitting at least one of the result data or the document data to the user device.
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公开(公告)号:US12106478B2
公开(公告)日:2024-10-01
申请号:US17203196
申请日:2021-03-16
CPC分类号: G06T7/0014 , G06N20/00 , G06T3/147 , G06T7/11 , G06T2207/20081 , G06T2207/20084 , G06T2207/30008
摘要: A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.
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9.
公开(公告)号:US12100115B2
公开(公告)日:2024-09-24
申请号:US17398549
申请日:2021-08-10
发明人: Raz Carmi
CPC分类号: G06T3/153 , G06T3/14 , G06T3/147 , G06T7/11 , G06T7/30 , G06T7/38 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/30004 , G06T2207/30008 , G06T2207/30056
摘要: A method includes applying both a first dedicated functional-anatomical registration scheme to a first volume of interest to deform the first volume of interest and a second dedicated functional-anatomical registration scheme to a second volume of interest to deform the second volume of interest, wherein the first volume of interest at least partially encompasses the second volume of interest. The method includes identifying or segmenting relevant organs or anatomical structures related to a first group and a second group in the first volume of interest and the second volume of interest, respectively; generating a spatially smooth-transition weight mask that gives higher weight to image data corresponding to the identified or segmented relevant organs or anatomical structures related to the first group and the second group; and generating a final cohesive registered image volume from the first image volume and the second image volume utilizing the spatially smooth-transition weight mask.
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公开(公告)号:US12062110B2
公开(公告)日:2024-08-13
申请号:US17250647
申请日:2019-08-16
申请人: SONY CORPORATION
发明人: Tooru Masuda
IPC分类号: G06N20/00 , G06F18/214 , G06T1/20 , G06T3/12 , G06T3/147 , G06T7/73 , G06V10/24 , G06V10/774
CPC分类号: G06T1/20 , G06F18/214 , G06N20/00 , G06T3/12 , G06T3/147 , G06T7/73 , G06V10/243 , G06V10/774 , G06T2207/20081 , G06T2207/30201
摘要: An image processing apparatus (100) according to the present disclosure includes: a learning-data creation unit (132) configured to perform projection transformation on image data including a target as a subject, the learning-data creation unit (132) being configured to create learning data including the target as correct data; and a model generation unit (133) configured to generate, based on the learning data created by the learning-data creation unit (132), a learned model for detecting the target included in input data that includes a wide angle view image and is input to the learned model, the wide angle view image being created by projection transformation identical in scheme to the projection transformation by which the learning data is created.
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