Beijing Baidu Netcom Science and Technology Co., Ltd.

Zhizhi Guo

Junyu Han

Jingtuo Liu

Yipeng Sun

Zhibin Hong

Borong Liang

The present application discloses an image recognition method, apparatus, device, and a computer storage medium, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. The method includes: performing organ recognition on a human face image and marking positions of the human facial five sense organs in the human face image, obtaining a marked human face image; inputting the marked human face image into a backbone network model and performing feature extraction, obtaining defect features of the marked human face image outputted by different convolutional neural network levels of the backbone network model; and fusing the defect features of different levels that are located in a same area of the human face image, obtaining a defect recognition result of the human face image.

Image recognition method, apparatus, device, and computer storage medium

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

Image recognition method and apparatus, device, and computer storage medium

A method, an apparatus, a device and a storage medium for three-dimensional reconstruction are provided. The method may include: acquiring a sequence of RGBD images of a target; determining a set of first three-dimensional key points of the target on each RGBD image for the each RGBD image in the sequence of the RGBD images; transferring the set of the first three-dimensional key points to a camera coordinate system to obtain a set of second three-dimensional key points of the target in the camera coordinate system; calculating an Euler angle of the each RGBD image based on the set of the second three-dimensional key points; selecting an RGBD image based on the Euler angle; and performing three-dimensional reconstruction of the target based on the selected RGBD image to obtain a three-dimensional model of the target.

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Beijing, China

Zhizhi Guo