公开(公告)号：US20240346630A1

公开(公告)日：2024-10-17

申请号：US18632564

申请日：2024-04-11

Applicant: FARO Technologies, Inc.

Inventor： Daniel Döring ,  Rasmus Debitsch

IPC: G06T5/73 ,  G01S17/42 ,  G01S17/86 ,  G06T3/4053 ,  G06T5/50

CPC classification number: G06T5/73 ,  G01S17/42 ,  G01S17/86 ,  G06T3/4053 ,  G06T5/50 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20084 ,  G06T2207/20201 ,  G06T2207/20212

Abstract: Examples described herein provide a computer-implemented method for sharpening an image acquired during movement of a three-dimensional (3D) coordinate measurement device. The method includes receiving the image from the 3D coordinate measurement device, wherein the image was acquired while the 3D coordinate measurement device was moving. The method further includes sharpening the image to generate a sharpened image based at least in part on at least one of movement information about the movement of the 3D coordinate measurement device or depth information. The method further includes performing, using the sharpened image, a scanning operation.

公开(公告)号：US12119090B1

公开(公告)日：2024-10-15

申请号：US18545399

申请日：2023-12-19

Applicant: Recursion Pharmaceuticals, Inc.

Inventor： Oren Zeev Kraus ,  Kian Runnels Kenyon-Dean ,  Mohammadsadegh Saberian ,  Maryam Fallah ,  Peter Foster McLean ,  Jessica Wai Yin Leung ,  Vasudev Sharma ,  Ayla Yasmin Khan ,  Jaichitra Balakrishnan ,  Safiye Celik ,  Dominique Beaini ,  Maciej Sypetkowski ,  Chi Cheng ,  Kristen Rose Morse ,  Maureen Katherine Makes ,  Benjamin John Mabey ,  Berton Allen Earnshaw

IPC: G06T15/00 ,  G06T5/73 ,  G16B20/00 ,  G16B40/00 ,  G16B45/00

CPC classification number: G16B45/00 ,  G06T5/73 ,  G16B20/00 ,  G16B40/00 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing generative machine learning models to generate embeddings from phenomic images (or other microscopy representations). For example, the disclosed systems can train a generative machine learning model (e.g., a masked autoencoder generative model) to generate predicted (or reconstructed) phenomic images from masked version of ground truth training phenomic images. In some cases, the disclosed systems utilize a momentum-tracking optimizer while reducing a loss of the generative machine learning model to enable efficient training on large scale training image batches. Furthermore, the disclosed systems can utilize Fourier transformation losses with multi-stage weighting to improve the accuracy of the generative machine learning model on the phenomic images during training. Indeed, the disclosed systems can utilize the trained generative machine learning model to generate phenomic embeddings from input phenomic images (for various phenomic comparisons).

公开(公告)号：US12118701B2

公开(公告)日：2024-10-15

申请号：US17421981

申请日：2020-01-08

Applicant: AGFA NV

Inventor： Marc Cresens

IPC: G06T7/00 ,  G01N23/04 ,  G01N23/083 ,  G06T5/73

CPC classification number: G06T7/0002 ,  G01N23/04 ,  G01N23/083 ,  G06T5/73 ,  G06T7/97 ,  G01N2223/04 ,  G01N2223/40 ,  G06T2207/10116 ,  G06T2207/20032 ,  G06T2207/30168

Abstract: The invention is related to a method for automatic selection and pre-processing of digital images that comprise the necessary amount of Transfer function modulated quantum-noise to apply a mathematical sharpness calculation method for calculation of a sharpness parameter of the digital imaging system. Suitable images for the method are selected from the pool of available images acquired by the digital imaging system during daily operation.

公开(公告)号：US12112461B2

公开(公告)日：2024-10-08

申请号：US17824064

申请日：2022-05-25

Applicant: REALTEK SEMICONDUCTOR CORP.

Inventor： Sheng-Kai Lin ,  Min-Chen Hsu ,  Pao-Chi Yeh ,  Kai-Wen Lai ,  Chen-Chieh Yao

IPC: G06T7/13 ,  G06T5/20 ,  G06T5/73 ,  G06T5/92 ,  G06T7/11

CPC classification number: G06T5/92 ,  G06T5/20 ,  G06T5/73 ,  G06T7/11 ,  G06T7/13 ,  G06T2207/20021

Abstract: An adaptive image shading correction method and an adaptive image shading system are provided. The method includes: configuring an image capturing device to obtain a current frame; and configuring a processing unit to: divide the current frame into blocks; select block pairs from the blocks, in which each of the block pairs includes an inner block and an outer block; perform a filtering process for each of the block pairs to determine whether a brightness condition, a saturation condition, a hue similarity condition, and a sharpness similarity condition are met; in response to obtaining filtered block pairs, calculate a sum similarity threshold based on hue statistical data, a saturation difference, and a brightness difference; and use filtered blocks with individual thresholds less than the sum similarity threshold to calculate a shadow compensation value to adjust the current frame.

公开(公告)号：US20240320822A1

公开(公告)日：2024-09-26

申请号：US18278323

申请日：2021-04-15

Applicant: SAMSUNG LIFE PUBLIC WELFARE FOUNDATION

Inventor： Kyoung-Mee KIM

IPC: G06T7/00 ,  G06T5/73 ,  G06T7/11 ,  G06V10/24

CPC classification number: G06T7/0012 ,  G06T5/73 ,  G06T7/11 ,  G06V10/24 ,  G06T2207/30024 ,  G06T2207/30096

Abstract: A deep learning model-based tumor-stroma ratio prediction method comprises the steps in which an analysis device: receives an input of a first staining image of target tissue; generates a second staining image by inputting the first staining image into a trained deep learning model; generates a first binary image of the first staining image; generates a second binary image of the second staining image; and calculates the tumor-stroma ratio of the target tissue on the basis of the first binary image and the second binary image.

公开(公告)号：US12100127B2

公开(公告)日：2024-09-24

申请号：US17480281

申请日：2021-09-21

Applicant: Boston Scientific Scimed, Inc.

Inventor： Kirsten Viering ,  Louis J. Barbato ,  George Wilfred Duval

IPC: G06T5/92 ,  G06T5/73 ,  G06T7/90

CPC classification number: G06T5/92 ,  G06T5/73 ,  G06T7/90 ,  G06T2207/10068 ,  G06T2207/20221

Abstract: A medical device includes a sensor, a processor, and non-transitory computer readable medium storing imaging instructions. The sensor captures a first raw image into a first frame of raw pixel values including a plurality of first pixel values that represent pixels of a first color and a plurality of second pixel values that represent pixels of a second color. The sensor captures a second raw image into a second frame of raw pixel values including a plurality of third pixel values that represent pixels of the second color and a plurality of fourth pixel values that represent pixels of a third color. The processor convolves the first frame of raw pixel values with the second frame of raw pixel values to generate a first extrapolated frame of pixel values in a YCrCb format and creates a processed image from the first extrapolated frame of pixel values.

公开(公告)号：US20240311982A1

公开(公告)日：2024-09-19

申请号：US18218266

申请日：2023-07-05

Applicant: RESEARCH & BUSINESS FOUNDATION SUNGKYUNKWAN UNIVERSITY

Inventor： Byeung Woo JEON ,  Jae Lin LEE

IPC: G06T5/00 ,  G06T3/40 ,  G06T7/20 ,  G06V10/141 ,  G06V10/56

CPC classification number: G06T5/73 ,  G06T3/4015 ,  G06T7/20 ,  G06V10/141 ,  G06V10/56

Abstract: A method of reconstructing an image using motion deblurring according to the present invention includes the steps of: modulating a first raw image using Coded Exposure Photography (CEP); and decomposing the first raw image into at least one color channel image, wherein the step of modulating a first raw image using Coded Exposure Photography (CEP) includes modulating the first raw image using a coded pattern determined according to a motion and arrangement of a color filter array (CFA).

公开(公告)号：US20240303829A1

公开(公告)日：2024-09-12

申请号：US18443422

申请日：2024-02-16

Applicant: CANON KABUSHIKI KAISHA ,  CANON MEDICAL SYSTEMS CORPORATION

Inventor： Ryuichi NANAUMI ,  Kazuhiko FUKUTANI ,  Kazuya OKAMOTO

IPC: G06T7/246 ,  G06T5/50 ,  G06T5/73

CPC classification number: G06T7/246 ,  G06T5/50 ,  G06T5/73 ,  G06T2207/10088 ,  G06T2207/20201 ,  G06T2207/30004 ,  G06T2207/30196 ,  G06T2207/30204

Abstract: An object motion measurement apparatus includes an imaging device which images an object; and at least one memory and at least one processor which function as: a body motion acquisition unit configured to acquire information related to a motion of the object from an image imaged by the imaging device; an index acquisition unit configured to acquire from the image imaged by the imaging device an index which reflects vibration of an imaging apparatus, which is different from the imaging device and images the object; and an output unit configured to output the index to the imaging apparatus.

公开(公告)号：US12086964B2

公开(公告)日：2024-09-10

申请号：US18371347

申请日：2023-09-21

Applicant: Align Technology, Inc.

Inventor： Chao Shi ,  Yingjie Li ,  Chad Clayton Brown ,  Christopher E. Cramer

IPC: G06T5/73 ,  G06N20/00 ,  G06T11/20 ,  G06V10/98 ,  G06V40/16

CPC classification number: G06T5/73 ,  G06N20/00 ,  G06T11/20 ,  G06V10/993 ,  G06V40/165 ,  G06V40/166 ,  G06T2210/12

Abstract: A processing device receives an image and calculates a sharpness metric for a region of interest of the image. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

公开(公告)号：US12086958B2

公开(公告)日：2024-09-10

申请号：US17977604

申请日：2022-10-31

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Kyuhwan Choi ,  Jingu Heo

IPC: G06T5/50 ,  G06T3/4053 ,  G06T5/73 ,  H04N23/62

CPC classification number: G06T5/50 ,  G06T3/4053 ,  G06T5/73 ,  H04N23/62 ,  G06T2207/20221

Abstract: A method and device with synthetic image generation is included. A method includes acquiring a first image captured by a first camera and second images captured by a second camera having a narrower field of view (FOV) when the second images are captured thereby than that of the first camera when the first image is captured thereby. The method also includes detecting respective blur statuses of each of the second images, selecting a region of the first image on the basis of the region corresponding to a second image, of the second images, selected based on a determination that the blur status of the second image satisfies a blur condition, and generating a synthetic image by synthesizing together the selected region of the first image and the second images not including the selected second image.