Boe Technology Group Co., Ltd.

Jibo Zhao

Yue Li

Bingchuan Shi

Ruibin Xue

Guangwei Huang

Jing Yu

Xiaojun Tang

Xingxing Zhao

Honghong Jia

Lei Cao

Yingjie Li

Xuewen Lv

Xingqun Jiang

Xiaoli Jin

A translation pen includes: a pen body, an indication component, an image collector and a first processor. The pen body has a pen tip end. The indication component is arranged on the pen tip end. The image collector is arranged on the pen body, and the image collector is configured to: collect an image including a text to be translated according to a position indicated by the indication component, and send the image collected. The first processor is arranged in the pen body and electrically connected to the image collector, and the first processor is configured to: receive the image sent by the image collector, and recognize the text to be translated in the image.

Translation pen and control method therefor

A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

Method and apparatus for recognizing imaged information-bearing medium, computer device and medium

A character recognition method includes: performing feature extraction on an image to be recognized to obtain a first feature map; processing the first feature map to at least obtain N first candidate carrier detection boxes, each first candidate carrier detection box being configured to outline a region of a character carrier; screening the N first candidate carrier detection boxes to obtain K first target carrier detection boxes; performing a feature extraction on the first feature map to obtain a second feature map; processing the second feature map to obtain L first candidate character detection boxes, each first candidate character detection box being configured to outline a region containing at least one character; screening the L first candidate character detection boxes to obtain J first target character detection boxes; and recognizing characters in the J first target character detection boxes to obtain J target character informations.

Character recognition method and terminal device

The present disclosure provides an answer evaluation method, an answer evaluation system, an electronic device, and a medium. The method comprises: acquiring an answer image, for a test paper answered by a use; classifying the answer image based on a pre-trained test question classification model, so as to obtain an objective question answer area and a subjective question answer area; identifying at least one objective question in the objective question answer area and an objective question answers for each of the at least one objective question; identifying at least one subjective question in the subjective question answer area and a subjective question answers for each of the at least one subjective question; and determining a total score value of the test paper based on the objective question, the objective question answer, the subjective question and the subjective question answer.

Answer evaluation method, answer evaluation system, electronic device, and medium

A gesture control method comprises: acquiring an image; performing a gesture detection on the image to recognize a gesture from the image; determining, if no gesture is recognized from the image, whether a time interval from a last gesture detection, in which a gesture was recognized, to the gesture detection is less than a preset time period; tracking, if the time interval is less than the preset time period, the gesture in the image based on a comparative gesture which is a gesture recognized last time or tracked last time; and updating the comparative gesture with a currently recognized gesture or a currently tracked gesture. A gesture control method is provided that comprises: identifying an image acquisition device for acquiring image, to determine whether the image acquisition device is a depth-of-field image acquisition device, and performing gesture control based on the identification.

Recognizing and tracking gestures

A three-dimensional reconstruction method includes: acquiring first images captured by a first camera, each of the first images being an image containing a target object; determining a shooting angle of the each of the first images, the shooting angle being configured to characterize a shooting direction relative to the target object when the first camera shoots the first images; determining an angle interval corresponding to the each of the first images from a plurality of angle intervals included in an angle range [0, 360&#xb0;) based on the shooting angle, and setting the first image as a target image in the angle interval; and three-dimensionally reconstructing the target object based on the target images in the respective angle intervals to obtain a three-dimensional image of the target object.

Three-dimensional reconstruction method, apparatus and system, model training method and storage medium

A computer-implemented method of alerting a driver of a vehicle is provided. The computer-implemented method includes obtaining a plurality of consecutive input images during a time interval using a three-dimensional depth camera, a respective one of the plurality of consecutive input images comprising a user sub-image and an object sub-image; deriving three-dimensional coordinates of user feature points in the user sub-image of the respective one of the plurality of consecutive input images using a first classifier; deriving three-dimensional coordinates of object feature points in the object sub-image of the respective one of the plurality of consecutive input images using the first classifier; and classifying user posture in the respective one of the plurality of consecutive input images by analyzing the three-dimensional coordinates of user feature points and the three-dimensional coordinates of object feature points using a second classifier.

Computer-implemented method of alerting driver of vehicle, apparatus for alerting driver of vehicle, vehicle, and computer-program product

A sample extraction method targeting a video classification problem includes acquiring video data, parsing the video data to obtain a plurality of consecutive single-frame images corresponding to the video data, and extracting feature images from the plurality of consecutive single-frame images to compose samples, where the feature images are used to summarize content information of the video data, and the samples do not contain redundant image data.

Sample extraction method and device targeting video classification problem

A gesture detection method, a gesture processing device, and a computer readable storage medium. The gesture detection method includes: performing a shrinking process and a background modeling on a first frame of original image, to generate a first frame of background image; determining a motion region of the first frame of background image based on the first frame of background image; determining a first gesture detection area in the first frame of original image according to the motion region of the first frame of background image; and performing a gesture detection within the first gesture detection area.

Gesture detection method, gesture processing device, and computer readable storage medium

A gesture recognition method includes determining a palm connected domain based on an acquired depth image. The method includes determining a tracking frame corresponding to the palm connected domain. The method includes recognizing a gesture within a region of a to-be-recognized image corresponding to the tracking frame, based on a location of the tracking frame. In this arrangement, a palm connected domain and a tracking frame corresponding to the palm connected domain are acquired, and a gesture is recognized within a region of a to-be-recognized image corresponding to the tracking frame.

Gesture recognition method and apparatus, electronic device, and computer-readable storage medium

A method for video classification includes: extracting an original image and an optical flow image corresponding to a to-be-classified video from the to-be-classified video; inputting the original image to a space-domain convolutional neural network model to obtain a space-domain classification result corresponding to the to-be-classified video; inputting the optical flow image to a time-domain convolutional neural network model to obtain a time-domain classification result corresponding to the to-be-categorized video, wherein the time-domain convolutional neural network model and the space-domain convolutional neural network model are convolutional neural network models of different network architectures; and merging the space-domain classification result and the time-domain classification result to obtain a classification result corresponding to the to-be-classified video.

Method and device for video classification

A hand detection method, a hand segmentation method, an image detection method and system, a storage medium, and a device are provided. The image detection method includes: determining a first starting point in a connected domain of an image to be detected; determining n farthest extremum points different from the first starting point, wherein an Nth farthest extremum point is a pixel point in the connected domain having a maximum geodesic distance to an Nth starting point, an (N+1)th starting point is the Nth farthest extremum point, and n and N are both positive integers; performing region growing with the n farthest extremum points as initial points respectively, to acquire n regions in the connected domain; judging whether a relationship between a preset feature of each region and a preset feature of the connected domain satisfies a selection condition, to determine an available region satisfying the selection condition.

Hand detection method and system, image detection method and system, hand segmentation method, storage medium, and device

An image segmentation method, an image segmentation system, a storage medium and an apparatus including the image segmentation system or the storage medium are provided. The image segmentation method includes: obtaining a connected region where a target object is located from a depth image; determining a principal direction or a secondary direction of the connected region by a principal component analysis method; and acquiring an image of the target object from the connected region according to relationship between morphology of the target object and the principal direction or the secondary direction.

Image segmentation method, image segmentation system and storage medium and apparatus including the same

A method and a system for determining a datum plane are disclosed. The method for determining a datum plane includes: acquiring a depth image; performing edge extraction on the depth image to form an edge image, the edge image including a plurality of planar graphs; and selecting from the planar graphs in the edge image to determine the datum plane. The technical solutions provided by the disclosure can easily match the virtual object with the real scene in real time, and improve users' sensory experience beyond reality.

Method and system for determining datum plane

The present disclosure provides a depth map generation apparatus, including a camera assembly with at least three cameras, an operation mode determination module and a depth map generation module. The camera assembly with at least three cameras may a first camera, a second camera and a third camera that are sequentially aligned on a same axis. The operation mode determination module may be configured to determine an operation mode of the camera assembly. The operation mode includes at least: a first mode using images of non-adjacent cameras, and a second mode using images of adjacent cameras. Further, the depth map generation module may be configured to generate depth maps according to the determined operation mode.

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Beijing, China

Jibo Zhao