Abstract: Disclosed is a method for vein recognition, the method includes: performing a difference operation and a channel connection on two to-be-verified target vein images respectively to obtain a difference image and a two-channel image of the two target vein images; performing the channel connection on the obtained difference image and two-channel image to obtain a three-channel image, so as to use the three-channel image as an input of a CNN network; fine-tuning a pre-trained model SqueezeNet that completes training on an ImageNet; integrating the difference image and the three-channel image through a cascade optimization framework to obtain a recognition result; regarding a pair of to-be-verified images as a sample, transforming the sample, taking the transformed sample as the input of the CNN network, obtaining a recognition result by supervised training on the network.

Abstract: The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

Abstract: A double attention network (DANet)-based drone patrol and inspection system for coastline floating garbage, including: an image acquisition module configured to shoot a video of a coastline in need of patrol and inspection by using a drone, and obtain an image from the video; a feature extraction module configured to extract shallow features and deep features, fuse the shallow features and the deep features to obtain a shared feature, and finally output a panoramic recognition result; a network training module configured to perform training on the labeled image so that the network can recognize the coastline and floating garbage; and a path correction module configured to adjust a flying direction of the drone.

Abstract: A method and a device for acquiring three-dimensional coordinates of ore based on mining process are disclosed. The method includes: obtaining a two-dimensional coordinate of the ore by using a YOLACT algorithm and a NMS algorithm to obtain a prediction mask map, obtaining depth information of the ore based on the color map and the infrared depth map, and combining the two-dimensional coordinate with the depth information to obtain a three-dimensional coordinate of the ore.

Abstract: A double attention network (DANet)-based drone patrol and inspection system for coastline floating garbage, including: an image acquisition module configured to shoot a video of a coastline in need of patrol and inspection by using a drone, and obtain an image from the video; a feature extraction module configured to extract shallow features and deep features, fuse the shallow features and the deep features to obtain a shared feature, and finally output a panoramic recognition result; a network training module configured to perform training on the labeled image so that the network can recognize the coastline and floating garbage; and a path correction module configured to adjust a flying direction of the drone.

Abstract: Disclosed is a method for vein recognition, the method includes: performing difference operation and channel connection on two to-be-verified target vein images respectively to obtain a difference image and a two-channel image of the two target vein images; performing channel connection on the obtained difference image and two-channel image to obtain a three-channel image, so as to use the three-channel image as an input of a CNN network; fine-tuning a pre-trained model SqueezeNet that completes training on an ImageNet; integrating the difference image and the three-channel image through a cascade optimization framework to obtain a recognition result; regarding a pair of to-be-verified images as a sample, transforming the sample, taking the transformed sample as the input of the CNN network, obtaining a recognition result by supervised training on the network, which solve the problem of less training data and improve the accuracy of recognition with a small network storage capacity.

Abstract: A method for automatic shopping in a shopping mall, a storage medium, an electronic device, and a device are disclosed. After a consumer selects commodities from an input means, a path along which a moving means needs to travel to pick up the commodities from a shelf is planned on a global map according to first coordinates carried in commodity information, then the moving means is controlled to automatically walk in the shopping mall according to the planned path, the height of lifting means is adjusted according to height values of the corresponding commodities on the shelf, and when the moving means arrives at designated positions on the path, a pickup means on the lifting means picks up the commodities selected by the consumer from the shelf.

Abstract: An intelligent medical material supply robot based on Internet of Things and SLAM technology is disclosed, which realizes localization and mapping through a binocular camera and a lidar. A cloud data center schedules the medical material supply robot in real time according to material usage. The material supply robot receives corresponding scheduling information, and according to localization of the robot and map information, dynamically avoids obstacles by using a path planning algorithm to go to a designated floor for materials delivery.

Abstract: Disclosed are an education assisting robot and a control method thereof. The method includes: capturing and recognizing students' faces from shot images, and checking students' attendance; and capturing a teacher's face from the shot images and identifying a target, and target-following the teacher. The role of a target character can be automatically distinguished from the images, and different actions can be made for different target characters, including attendance checking and target following, so as to provide more different response functions and reduce the workload of teachers.

Abstract: A method and a device for acquiring three-dimensional coordinates of ore based on mining process are disclosed. The method includes: obtaining a two-dimensional coordinate of the ore by using a YOLACT algorithm and a NMS algorithm to obtain a prediction mask map, obtaining depth information of the ore based on the color map and the infrared depth map, and combining the two-dimensional coordinate with the depth information to obtain a three-dimensional coordinate of the ore.

Abstract: The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.