Samsung Electronics Co., Ltd.

Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

Jingtao Xu

Jae-Joon Han

Deheng Qian

Chao Zhang

Hao Feng

Byungin Yoo

Jianwen Suo

Changkyu Choi

Qiang Wang

Chang Kyu Choi

Jaejoon Han

Jiaqian Yu

Yaozu An

Yanhu Shan

Yue Li

Youngsung Kim

Byung In Yoo

Youngjun Kwak

Biao Wang

Yiwei Chen

Hui Zhang

Bo Xu

Tong Liu

HyunJeong Lee

Tianchu Guo

Keun Woo Park

Jia Qi Li

Jae Joon Han

Xiaojun Tang

Zhiguo Zhang

Sungun Park

Hyunsurk Eric Ryu

Hana Lee

Changbeom Park

Feng Shi

Dongqing Zou

Wonsuk Chang

Shaonan Zhang

Tingting Wang

Zhihua Liu

Yakun Wang

Hyunjeong Lee

Juwoan Yoo

Tushar Balasaheb Sandhan

Hyeongwook Yang

Weiheng Liu

Hangkai Tan

Hao Zhang

Hui Li

Lili Chen

Jihye Kim

Lin Ma

Wenhao Zhang

Bing Yu

Jinghua Miao

Wenyu Li

Xi Li

Bin Zhao

Jingu Heo

Xuefeng Wang

Kai Zhao

Hanwen Liu

Yukun Sun

Qingwen Fan

Zhifu Li

Jinbao Peng

He Zheng

Mingcai Zhou

Xiangyang Liu

Huanhuan Zhang

Dongkyung Nam

Zairan Wang

Guangwei Wang

Changwoo Shin

Feng Qiu

JinMan Park

Chyangbeom Park

A method with object tracking includes: determining a first target tracking state by tracking a target from a first image frame with a first field of view (FoV); determining a second FoV based on the first FoV and the first target tracking state; and generating a second target tracking result by tracking a target from a second image frame with the second FoV.

Method and apparatus with object tracking using dynamic field of view

A target object detection method and apparatus are provided. The target object detection method and apparatus are applicable to fields such as artificial intelligence, object tracking, object detection, and image processing. An object is detected from a frame image of a video including a plurality of frame images based on a target template set including one or more target templates.

Video object detection and tracking method and apparatus

Disclosed are face clustering method and apparatus, image classification storage method, computer-readable storage medium and electronic device. The face clustering method includes: clustering to-be-clustered images, including: acquiring similarity threshold corresponding to quantity level of current image categories in image category library, at least two quantity levels corresponding to different similarity thresholds; judging, according to current similarity threshold and similarity between each to-be-clustered face image and at least one image category in image category library, whether there is image of same category as to-be-clustered face image in image category library; determining, when there is image of same category as to-be-clustered face image, category label of the to-be-clustered face image according to category label of image of same category as to-be-clustered face image; and assigning, when there is no image of same category as to-be-clustered face image, category label to the to-be-clustered face image according to first preset rule.

Face clustering method and apparatus, classification storage method, medium and electronic device

The present disclosure provides a method and a device for determining a region to be cleaned, a dust cleaning apparatus, a terminal for controlling a cleaning robot, and a storage medium. The method includes: determining, in an initial image of a photovoltaic panel, a gap region associated with a gap between cell pieces; assigning first preset values to pixel values of pixels located in the gap region of the initial image to obtain a first value-assigned image, wherein the first preset values are pixel values of pixels in the initial image that correspond to the cell pieces.

Method and device for determining region to be cleaned, dust cleaning apparatus, terminal for controlling cleaning robot, and storage medium

An image retrieval method, an image retrieval device, and an image retrieval system, which are used for image retrieval, are provided. The image retrieval method comprises: receiving a first original image (S); extracting an image feature of the first original image to obtain a first feature code (S); obtaining first target information according to the first feature code (S); according to the first target information, searching for a first target painting set corresponding to the first target information in at least one of a painting library and a knowledge graph library, so as to obtain the first target painting set (S); and outputting the first target painting set (S).

Image retrieval method, image retrieval devices, image retrieval system and image display system

Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.

Method and apparatus with liveness verification

The present disclosure relates to a positioning method, a positioning server, and a positioning system. The positioning method includes: receiving a wireless positioning coordinate of a moving object; determining a visual sub-map to be matched in a visual map database based on the wireless positioning coordinate; and obtaining a visual positioning coordinates corresponding to a current image of the moving object captured at time of wireless positioning as a positioning result, based on the determined visual sub-map to be matched.

Positioning method, positioning server and positioning system

Disclosed is a target tracking method and apparatus. The target tracking apparatus includes a processor configured to obtain a first depth feature from a target region image and obtain a second depth feature from a search region image, obtain a global response diagram between the first depth feature and the second depth feature, acquire temporary bounding box information based on the global response diagram, updated the second depth feature based on the temporary bounding box information, obtain local feature blocks based on the first depth feature, obtain a local response diagram based on the local feature blocks and the updated second depth feature, and determine output bounding box information based on the local response diagram.

Method and apparatus for tracking target

An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Image processing method and apparatus for object detection

The embodiments of the present disclosure provide a method of registering a face based on video data, including: receiving video data; acquiring a first image frame sequence from the video data, wherein each image frame in the first image frame sequence includes a face detection frame containing a complete facial feature; determining whether each image frame reaches a preset definition or not according to a relative position of the face detection frame in the image frame; extracting a plurality of sets of facial features based on an image information of the plurality of face detection frames in response to determining that the image frame reaches the preset definition, and determining whether the faces represent an object or not according to the plurality of sets of facial features; and registering the object according to the first image frame sequence in response to determining that the faces represent the object.

Method and device of registering face based on video data, and electronic whiteboard

An image-to-image search method which is implemented in a server is provided. The server receives an image to be searched and at least one category of similarity search sent by a terminal. The server extracts a feature code of the image by using a trained hash model that uniquely corresponds to the at least one category of similarity search according to the received image and the at least one category of similarity search. The server obtains target images that are similar to the image according to the feature code of the image in a feature code library that uniquely corresponds to the at least one category of similarity search. The server sends the target images to the terminal.

Image-to-image search method, computer-readable storage medium and server

A liveness test method and apparatus is disclosed. The liveness test method includes detecting a face region in an input image for a test target, implementing a first liveness test to determine a first liveness value based on a first image corresponding to the detected face region, implementing a second liveness test to determine a second liveness value based on a second image corresponding to a partial face region of the detected face region, implementing a third liveness test to determine a third liveness value based on an entirety of the input image or a full region of the input image that includes the detected face region and a region beyond the detected face region, and determining a result of the liveness test based on the first liveness value, the second liveness value, and the third liveness value.

Liveness test method and apparatus

A processor implemented method of processing a facial expression image, the method includes controlling a camera to capture a first facial expression image and a second facial expression image, acquiring a first expression feature of the first facial expression image, acquiring a second expression feature of the second facial expression image, generating a new expression feature dependent on differences between the acquired first expression feature and the acquired second expression feature, and adjusting a target facial expression image based on the new expression feature.

Facial expression image processing method and apparatus

A depth estimation method and apparatus are provided. The depth estimation method includes obtaining an image from an image sensor comprising upper pixels, each comprising N sub-pixels, obtaining N sub-images respectively corresponding to the N sub-pixels from the image, obtaining a viewpoint difference between the N sub-images using a first neural network, and obtaining a depth map of the image based on the viewpoint difference using a second neural network.

Depth estimation method and apparatus

The present disclosure discloses an indoor positioning method, an indoor positioning system, an indoor positioning apparatus and a computer readable medium. The indoor positioning method includes: determining an initial position of a target object using combination of wireless positioning and visual feature map matching positioning; and according to the initial position of the target object, performing real-time positioning on the target object using combination of short-time interframe feature matching positioning and the visual feature map matching positioning.

Indoor positioning method, indoor positioning system, indoor positioning apparatus and computer readable medium

Growing community of inventors

Beijing, China

Jingtao Xu