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公开(公告)号：US11636698B2
公开(公告)日：2023-04-25
申请号：US17368917
申请日：2021-07-07
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Cheolhun Jang , Dokwan Oh , Dae Hyun Ji
Abstract: A method and apparatus for adjusting a neural network that classifies a scene of an input image into at least one class is provided. The method generates a feature image having a size that is less than a size of an input image by applying a convolutional network to the input image, determines at least one class corresponding to the feature image, generates a class image having a size corresponding to the size of the input image by applying a deconvolutional network to the feature image, calculates a loss of the class image based on a verification class image preset with respect to the input image, and adjusts the neural network based on the loss.
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公开(公告)号：US10885787B2
公开(公告)日：2021-01-05
申请号：US16014078
申请日：2018-06-21
Applicant: Samsung Electronics Co., Ltd.
Inventor： Jaewoo Lee , Cheolhun Jang , DongWook Lee , Wonju Lee , Dae Hyun Ji , Yoonsuk Hyun
Abstract: An object recognition method and apparatus are provided. The object recognition apparatus acquires localization information of a vehicle, acquires object information about an object located in front of the vehicle, determines a candidate region in which the object is predicted to exist in an image in front of the vehicle, based on the localization information and the object information, and recognizes the object in the image based on the candidate region.
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公开(公告)号：US12272158B2
公开(公告)日：2025-04-08
申请号：US17862821
申请日：2022-07-12
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Nayeon Kim , Moonsub Byeon , Dokwan Oh , Dae Hyun Ji
Abstract: A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.
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公开(公告)号：US11087185B2
公开(公告)日：2021-08-10
申请号：US16416898
申请日：2019-05-20
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Cheolhun Jang , Dokwan Oh , Dae Hyun Ji
Abstract: A method and apparatus for adjusting a neural network that classifies a scene of an input image into at least one class is provided. The method generates a feature image having a size that is less than a size of an input image by applying a convolutional network to the input image, determines at least one class corresponding to the feature image, generates a class image having a size corresponding to the size of the input image by applying a deconvolutional network to the feature image, calculates a loss of the class image based on a verification class image preset with respect to the input image, and adjusts the neural network based on the loss.
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Patent Agency Ranking
公开(公告)号：US11525688B2
公开(公告)日：2022-12-13
申请号：US16135160
申请日：2018-09-19
Applicant: Samsung Electronics Co., Ltd.
Inventor： Wonju Lee , Jahoo Koo , DongWook Lee , Jaewoo Lee , Dae Hyun Ji
Abstract: An object positioning method and apparatus is disclosed. The object positioning apparatus may obtain a reference position of an object, obtain a map-based heading angle of the object based on waypoints on a map, and determine a current position of the object based on the reference position and the map-based heading angle.
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公开(公告)号：US10599930B2
公开(公告)日：2020-03-24
申请号：US15992234
申请日：2018-05-30
Applicant: Samsung Electronics Co., Ltd.
Inventor： Wonju Lee , Jaewoo Lee , Dae Hyun Ji
Abstract: Disclosed is a method and apparatus of detecting an object of interest, where the apparatus acquires an input image, sets a region of interest (ROI) in the input image, and detects the object of interest from a restoration image, having a resolution greater than a resolution of the input image, corresponding to the ROI.
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公开(公告)号：US10853667B2
公开(公告)日：2020-12-01
申请号：US16166504
申请日：2018-10-22
Applicant: Samsung Electronics Co., Ltd.
Inventor： Dae Hyun Ji , Dokwan Oh , Jahoo Koo , Dongwook Lee , Wonju Lee , Jaewoo Lee , Cheolhun Jang , Yoonsuk Hyun
Abstract: Disclosed is a method and apparatus that includes acquiring a driving image; acquiring positioning information indicating a location of a vehicle; extracting map information corresponding to the positioning information; determining a regression line function corresponding to a road on which the vehicle is traveling based on the map information; detecting the linearity of the road from the driving image using the regression line function; and indicating the detected linearity.
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公开(公告)号：US10657387B2
公开(公告)日：2020-05-19
申请号：US15472517
申请日：2017-03-29
Applicant: Samsung Electronics Co., Ltd.
Inventor： Dae Hyun Ji , Dokwan Oh , Dongwook Lee , Jaewoo Lee , Cheolhun Jang
Abstract: A method and apparatus for controlling a vision sensor are provided. The apparatus and corresponding method are configured to predict an expected point, on a traveling path of a host vehicle, at which an illumination variation greater than or equal to a threshold is expected to occur, and determine whether the host vehicle is located within a threshold distance. The apparatus and corresponding method are also configured to control a vision sensor in the host vehicle based on the expected illumination variation in response to the host vehicle being located within the threshold distance.
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公开(公告)号：US10579058B2
公开(公告)日：2020-03-03
申请号：US15704282
申请日：2017-09-14
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Dokwan Oh , Dae Hyun Ji
IPC: G05D1/08 , G05D1/00 , G06N3/02 , G06F17/18 , G06T7/70 , G06N7/00 , G06K9/62 , G06K9/00 , G06F16/00
Abstract: A training data generation method includes acquiring an image captured from a vehicle and location information of the vehicle corresponding to the image; acquiring map data corresponding to the acquired location information; determining truth data including information associated with a road included in the image from the acquired map data; and generating training data including the image and the determined truth data.
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