Error modeling method and device for prediction context of reversible image watermarking

    公开(公告)号:US11321796B2

    公开(公告)日:2022-05-03

    申请号:US16753220

    申请日:2018-09-27

    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.

    METHOD AND APPARATUS FOR ENHANCING SEMANTIC FEATURES OF SAR IMAGE ORIENTED SMALL SET OF SAMPLES

    公开(公告)号:US20210003700A1

    公开(公告)日:2021-01-07

    申请号:US16532375

    申请日:2019-08-05

    Abstract: The present disclosure relates to a method for enhancing sematic features of SAR image oriented small set of samples, comprising: acquiring a sample set of an SAR target image, and performing transfer learning and training on the sample set to obtain a initialized deep neural network of an SAR target image, the sample set comprising an SAR target image and an SAR target virtual image; performing network optimization on the deep neural network by an activation function, and extracting features of the SAR target image by the optimized deep neural network to obtain a feature map; and mapping, by an auto-encoder, the feature map between a feature space and a semantic space to obtain a deep visual feature with an enhanced semantic feature.

    Device for measuring speed of tennis ball

    公开(公告)号:US11266895B2

    公开(公告)日:2022-03-08

    申请号:US16937222

    申请日:2020-07-23

    Abstract: Disclosed is a device for measuring speed of a tennis ball, which includes: a housing, a power supply, a main control device, a sensor device, a transmission device, a clamping device and a limiting device. The clamping device is clamped at the serve opening of a tennis ball machine, which can detect the serve speed of a tennis ball accurately with simple operation. The transmission device is engaged with the second sensor device, and the housing is slidably connected with the second sensor device. The emitting ends of the second sensor device and the first sensor device are positioned in the same vertical plane. The limiting device is connected with the second sensor device to limit excessive operation of the transmission device.

    METHOD AND APPARATUS FOR MULTI-SCALE SAR IMAGE RECOGNITION BASED ON ATTENTION MECHANISM

    公开(公告)号:US20210012146A1

    公开(公告)日:2021-01-14

    申请号:US16530766

    申请日:2019-08-02

    Abstract: Disclosed are a method and an apparatus for multi-scale SAR image recognition based on attention mechanism. According to the method, a whole image recognition network is adjusted by training a SAR training image by an attention prediction subnet, a region-of-interest positioning subnet and an image classification subnet in combination with a network loss, which greatly improves a network performance; and in addition, an attention prediction map is generated by attention mechanism to position a most prominent feature part in the SAR image, which greatly eliminates a redundancy of image features in a machine vision, effectively determines a region-of-interest, reduces interference of image noises, greatly reduces an image processing time, improves a target recognition accuracy, is beneficial to next target positioning, and has a significant improvement on a network recognition speed integrally.

    METHOD AND APPARATUS FOR END-TO-END SAR IMAGE RECOGNITION, AND STORAGE MEDIUM

    公开(公告)号:US20210003697A1

    公开(公告)日:2021-01-07

    申请号:US16532361

    申请日:2019-08-05

    Abstract: Disclosed are a method and an apparatus for end-to-end SAR image recognition, and a storage medium. According to the disclosure, a generative adversarial network is used to enhance data and improve data richness of a SAR image, which is beneficial to subsequent network training; a semantic feature enhancement technology is also introduced to enhance semantic information of a SAR deep feature by a coding-decoding structure, which improves performances of SAR target recognition; and meanwhile, an end-to-end SAR image target recognition model with high integrity for big scenes like the Bay Area is constructed, which is helpful to improve a synthetic aperture radar target recognition model for big scenes like the Bay Area from local optimum to global optimum, increases the stability and generalization ability of the model, reduces the network complexity, and improves the target recognition accuracy.

    Method for vein recognition, and apparatus, device and storage medium thereof

    公开(公告)号:US11328418B2

    公开(公告)日:2022-05-10

    申请号:US16831239

    申请日:2020-03-26

    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.

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