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公开(公告)号：US20190042860A1
公开(公告)日：2019-02-07
申请号：US15992234
申请日：2018-05-30
Applicant: Samsung Electronics Co., Ltd.
Inventor： Wonju LEE , Jaewoo LEE , Dae Hyun JI
CPC classification number: G06K9/00798 , G06K9/00671 , G06K9/00825 , G06K9/3233 , G06K9/6232 , G06T3/40 , G06T3/4053 , G06T7/11 , G06T2207/10012 , G06T2207/20081 , G06T2207/30256 , G06T2210/12
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：US20240151550A1
公开(公告)日：2024-05-09
申请号：US18349393
申请日：2023-07-10
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Jahoo KOO , Jaemuk JEONG , Seho SHIN , Dae Hyun JI
CPC classification number: G01C21/3811 , B60W60/001 , G01C21/3848 , G06T7/70 , G06V20/584 , B60W2420/42 , B60W2555/60 , B60W2556/40 , G06T2207/30252
Abstract: A method of determining a signal state of a target traffic light includes: acquiring an isolated image of the target traffic light from an input image of the target traffic light, wherein the input image is captured when the vehicle is at a position, wherein the isolated image is acquired using image processing on the input image, the input image having been captured by a camera module of the vehicle; acquiring information about the target traffic light based on a map position in map data, the map position corresponding to the position of the vehicle when the input image is acquired; and determining the signal state of the target traffic light based on the isolated image of the target traffic light and based on the information about the target traffic light.
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公开(公告)号：US20210334609A1
公开(公告)日：2021-10-28
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：US20190080608A1
公开(公告)日：2019-03-14
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：US20190205663A1
公开(公告)日：2019-07-04
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
CPC classification number: G06K9/00798 , G01S19/07 , G05D1/0212 , G06K9/3241
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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Patent Agency Ranking
公开(公告)号：US20190095809A1
公开(公告)日：2019-03-28
申请号：US15992240
申请日：2018-05-30
Applicant: Samsung Electronics Co., Ltd.
Inventor： Yoonsuk HYUN , Jaewoo LEE , Cheolhun JANG , Dae Hyun JI
Abstract: Disclosed is vehicle movement prediction method and apparatus for identifying a type of a target vehicle traveling in a target lane on a road and generating movement prediction information to predict a movement of the target vehicle based on the type of the target vehicle, wherein the movement is associated with the target lane.
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公开(公告)号：US20180060675A1
公开(公告)日：2018-03-01
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
CPC classification number: G06K9/00791 , G01C21/26 , G01S19/42 , G05D1/0246 , G06K9/2027 , H04N5/2351 , H04N5/2355 , H04N5/238
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：US20250157195A1
公开(公告)日：2025-05-15
申请号：US18897759
申请日：2024-09-26
Inventor： Sujin JANG , Sangpil KIM , Jinkyu KIM , Won Jeong RYOO , Dongwook LEE , Gyusam CHANG , Dae Hyun JI
IPC: G06V10/774 , G06V10/77
Abstract: A processor-implemented method including text-guided training using a pre-trained text-guided model and an image feature extractor based on one or more text inputs and one or more image inputs corresponding to the one or more text inputs, light detection and ranging (LiDAR)-guided training using a point cloud encoder and a bird's-eye view (BEV) encoder, and training an object detection model based on a result of the text-guided training and a result of the LiDAR-guided training, and the text-guided training includes outputting one or more text-image features that are used to train the object detection model by using the text-guided model and the image feature extractor.
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公开(公告)号：US20240161442A1
公开(公告)日：2024-05-16
申请号：US18451287
申请日：2023-08-17
Inventor： Sujin JANG , Sangpil KIM , Jinkyu KIM , Wonseok ROH , Gyusam CHANG , Dongwook LEE , Dae Hyun JI
CPC classification number: G06V10/25 , G01S17/86 , G01S17/89 , G06V10/44 , G06V10/761 , G06V10/764 , G06V10/82 , G06V2201/07
Abstract: A method and apparatus with object detector training is provided. The method includes obtaining first input data and second input data from a target object; obtaining second additional input data by performing data augmentation on the second input data; extracting a first feature to a shared embedding space by inputting the first input data to a first encoder; extracting a second feature to the shared embedding space by inputting the second input data to a second encoder; extracting a second additional feature to the shared embedding space by inputting thesecond additional input data to the second encoder; identifying a first loss function based on the first feature, the second feature, and the second additional feature; identifying a second loss function based on the second feature and the second additional feature; and updating a weight of the second encoder based on the first loss function and the second loss function.
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10.发明申请公开(公告)号：US20200210811A1
公开(公告)日：2020-07-02
申请号：US16553466
申请日：2019-08-28
Applicant: Samsung Electronics Co., Ltd.
Inventor： Jahoo KOO , Dae Hyun JI , Yoonsuk HYUN
Abstract: Provided is a method of processing data based on a neural network, the method including receiving input data; determining a hyper parameter of a first neural network that affects at least one of a speed of the first neural network and an accuracy of the first neural network by processing the input data based on a second neural network; and processing the input data based on the hyper parameter and the first neural network.
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