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公开(公告)号：US11373305B2
公开(公告)日：2022-06-28
申请号：US16989474
申请日：2020-08-10
Inventor： Ruixin Zhang , Xinyang Jiang , Xing Sun , Xiaowei Guo
Abstract: An image processing method is provided, including: obtaining a target image; invoking an image recognition model including: a backbone network, a pooling module and a dilated convolution module that are connected to the backbone network and that are parallel to each other, and a fusion module connected to the pooling module and the dilated convolution module; performing feature extraction on the target image by extracting, using the backbone network, a feature map of the target image, separately processing, using the pooling module and the dilated convolution module, the feature map, to obtain a first result outputted by the pooling module and a second result outputted by the dilated convolution module, and fusing the first result and the second result by using the fusion module into a model recognition result of the target image; and determining a semantic segmentation labeled image of the target image based on the model recognition result.
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公开(公告)号：US11328427B2
公开(公告)日：2022-05-10
申请号：US16578672
申请日：2019-09-23
Inventor： Xing Sun , Rui Wang , Xiaowei Guo
Abstract: Provided is a border detection method, server, and storage medium. The method including detecting a plurality of first straight line segments in a to-be-detected image, the to-be-detected image comprising a target region of a to-be-determined border; generating a plurality of first candidate borders of the target region according to the plurality of first straight line segments; obtaining a plurality of second candidate borders of the target region from the plurality of first candidate borders; extracting border features of the plurality of second candidate borders; and obtaining an actual border of the target region from the plurality of second candidate borders according to the border features of the plurality of second candidate borders and a border detection model, the border detection model being used to determine a detected value of each candidate border, and the detected value representing a similarity between each candidate border and the actual border.
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公开(公告)号：US11488302B2
公开(公告)日：2022-11-01
申请号：US16985966
申请日：2020-08-05
Inventor： Rui Wang , Xing Sun , Xiaowei Guo
Abstract: An object recognition method is performed at an electronic device. The method includes: pre-processing a target image, to obtain a pre-processed image, the pre-processed image including three-dimensional image information of a target region of a to-be-detected object, processing the pre-processed image by using a target data model, to obtain a target probability, the target probability being used for representing a probability that an abnormality appears in a target object in the target region of the to-be-detected object; and determining a recognition result of the target region of the to-be-detected object according to the target probability, the recognition result being used for indicating the probability that the abnormality appears in the target region of the to-be-detected object. The object recognition method can effectively improve accuracy of object recognition and avoid a case of incorrect recognition.
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公开(公告)号：US11270169B2
公开(公告)日：2022-03-08
申请号：US16942383
申请日：2020-07-29
Inventor： Xing Sun , Yi Zhang , Xinyang Jiang , Xiaowei Guo , Xuan Zhou , Jia Chang
Abstract: This application provides an image recognition method, a storage medium, and a computer device. The method includes: obtaining a to-be-recognized image; preprocessing the to-be-recognized image, to obtain a preprocessed image; obtaining, through a first submodel in a machine learning model, a first image feature corresponding to the to-be-recognized image, and obtaining, through a second submodel in the machine learning model, a second image feature corresponding to the preprocessed image; and determining, according to the first image feature and the second image feature, a first probability that the to-be-recognized image belongs to a classification category corresponding to the machine learning model. It may be seen that, the solutions provided by this application can improve recognition efficiency and accuracy.
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