公开(公告)号：US12229672B2

公开(公告)日：2025-02-18

申请号：US17451778

申请日：2021-10-21

Applicant: EMC IP Holding Company LLC

Inventor： Pablo Nascimento da Silva ,  Hugo de Oliveira Barbalho

IPC: G06V10/00 ,  G06F18/214 ,  G06F18/241 ,  G06N3/08 ,  G06V10/75

Abstract: One example method includes gathering, by a domain adversarial neural network model deployed in an autonomous vehicle operating in a domain, a dataset that comprises unsegmented and unlabeled image data about the domain, sampling the dataset to create an adapted domain dataset, detaching a domain classifier from the domain adversarial neural network, using the domain classifier as a domain change detector model to predict a class of the unsegmented and unlabeled image data in the adapted domain dataset, and based on the class, either: determining that the domain is changed or is unknown; or, determining that the domain has not changed.

公开(公告)号：US12217339B2

公开(公告)日：2025-02-04

申请号：US18272298

申请日：2022-03-31

Applicant: CARNEGIE MELLON UNIVERSITY

Inventor： Marios Savvides ,  Uzair Ahmed

IPC: G06V10/00 ,  G06T3/60 ,  G06T11/60 ,  G06T17/00 ,  G06T19/20 ,  G06V10/24 ,  G06V10/25 ,  G06V10/44 ,  G06V10/56 ,  G06V10/74 ,  G06V10/764 ,  G06V10/82 ,  G06V20/50 ,  G06V20/68

Abstract: A method for increasing the confidence of a match between a test image and an image stored in a library database in which features are extracted from the test image and compared to features stored in the image database. If a match is determined, one or more transformations are performed on the test image to generate pose-altered images from which features are extracted and matched with pose-altered images in the database. The scores for the subsequent matchings can be aggregated to determine an overall probability of a match between the test image in an image in the library database.

公开(公告)号：US12205252B2

公开(公告)日：2025-01-21

申请号：US17847101

申请日：2022-06-22

Applicant: Western Digital Technologies, Inc.

Inventor： Daniel Joseph Linnen ,  Ramanathan Muthiah ,  Kirubakaran Periyannan ,  Nikita Thacker

IPC: G06V10/00 ,  G06T5/50 ,  G06T7/194 ,  G06T7/90

Abstract: Bit-flip object insertion techniques are provided for use with a non-volatile memory (NVM) wherein an object is inserted into a background image by flipping or inverting one or more bits within the pixels of the background image that correspond to the shape and insertion location of an object being inserted. In an illustrative example, pixels within the background image that correspond to the shape and insertion location of the object are XORed with binary 1s. This flips the bits of those pixels to change the color (hue) and/or intensity (brightness) of the pixels so the object appears in the background image. In other examples, only the most significant bits of pixels in the background image are inverted (flipped). Exemplary latch-based procedures are described herein for high-speed processing on an NVM die. Multiple plane NVM die implementations are also described for massive processing.

公开(公告)号：US12200269B2

公开(公告)日：2025-01-14

申请号：US18585898

申请日：2024-02-23

Applicant: Sun Patent Trust

Inventor： Semih Esenlik ,  Matthias Narroschke ,  Thomas Wedi

IPC: G06V10/00 ,  H04N19/174 ,  H04N19/30 ,  H04N19/423 ,  H04N19/436 ,  H04N19/46 ,  H04N19/52 ,  H04N19/597 ,  H04N19/70

Abstract: A dependency indication is signaled within the beginning of a packet, that is, within the adjacent of a slice header to be parsed or a parameter set. This is achieved, for example, by including the dependency indication at the beginning of the slice header, preferably after a syntax element identifying the parameter set and before the slice address, by including the dependency indication before the slice address, by providing the dependency indication to a NALU header using a separate message, or by using a special NALU type for NALUs carrying dependent slices.

公开(公告)号：US12198387B2

公开(公告)日：2025-01-14

申请号：US17700765

申请日：2022-03-22

Applicant: Samsung Electronics Co., Ltd.

Inventor： Joohyun Lee

IPC: G06V10/00 ,  G06T1/20 ,  G06T5/70 ,  G06T7/90

Abstract: An application processor includes a neural processing unit configured to convert an input image into a first image based on a first pattern and generate a second image using a neural network, the second image compensating for the conversion; and an image signal processor including a plurality of pipelines configured to perform image signal processing, the plurality of pipelines including at least a first pipeline configured to receive the first image and a second pipeline configured to receive the second image.

公开(公告)号：US12190616B2

公开(公告)日：2025-01-07

申请号：US17683812

申请日：2022-03-01

Applicant: Carl Zeiss Microscopy GmbH

Inventor： Manuel Amthor ,  Daniel Haase ,  Michael Gögler

IPC: G06V10/00 ,  G06T3/40 ,  G06T7/00 ,  G06T7/62 ,  G06V20/69

Abstract: Various examples relate to determining a number and/or a confluency of cells in a microscopy image. To that end, the microscopy image is firstly rescaled and then processed.

公开(公告)号：US12178759B2

公开(公告)日：2024-12-31

申请号：US18598110

申请日：2024-03-07

Applicant: Augustine Biomedical + Design, LLC

Inventor： Scott D. Augustine ,  Susan D. Augustine ,  Garrett J. Augustine ,  Brent M. Augustine ,  Ryan S. Augustine ,  Randall C. Arnold

IPC: A61G13/10 ,  A61B46/10 ,  A61B50/13 ,  A61B50/15 ,  A61B90/50 ,  A61M16/01 ,  A61M16/06 ,  A61M16/18 ,  B01D46/00 ,  G06F16/10 ,  G06F16/48 ,  G06T7/70 ,  G06V10/00 ,  G16H10/60 ,  G16H20/17 ,  G16H20/40 ,  G16H30/20 ,  G16H30/40

Abstract: Module for housing electronic and electromechanical medical equipment including a portable digital camera and processing circuitry with machine vision and machine learning software for automatically documenting healthcare events and healthcare equipment operations in the electronic health record.

公开(公告)号：US12165381B2

公开(公告)日：2024-12-10

申请号：US17540112

申请日：2021-12-01

Applicant: CANON KABUSHIKI KAISHA

Inventor： Midori Inaba

IPC: G06V10/00 ,  G06T9/00 ,  G06V10/60

Abstract: An apparatus includes an acquisition unit to acquires normal information indicating a normal direction on a surface of an object and specular reflection information regarding reflection on the object in a specular reflection direction, and a compression unit to compress the normal information based on the specular reflection information and to perform a higher compression process for a lower specular reflection intensity on the object.

公开(公告)号：US12154309B2

公开(公告)日：2024-11-26

申请号：US18462305

申请日：2023-09-06

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V10/00 ,  G06F18/243 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/82 ,  G06V20/10 ,  G06V20/70 ,  G06V30/19 ,  G06V30/24

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

公开(公告)号：US12131525B2

公开(公告)日：2024-10-29

申请号：US17620142

申请日：2020-06-25

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Alexandra Groth ,  Axel Saalbach ,  Ivo Matteo Baltruschat ,  Jens Von Berg ,  Michael Grass

IPC: G06V10/00 ,  G06T7/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V10/96 ,  G06V20/70

CPC classification number: G06V10/82 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/774 ,  G06V10/96 ,  G06V20/70 ,  G06T2207/10081 ,  G06T2207/10124 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06V2201/03

Abstract: Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (IL) by labeling the first image data (I) and determining second synthesized labeled image data (ISL) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (IL) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.