Method for processing image, electronic device, and storage medium

    公开(公告)号:US11954836B2

    公开(公告)日:2024-04-09

    申请号:US17405820

    申请日:2021-08-18

    IPC分类号: G06T5/70

    摘要: A method, electronic device and storage medium for processing an image using depth-of-field information is disclosed. The method includes obtaining a weight matrix and a weight image based on depth-of-field data of an input image; obtaining a first horizontal summed area table corresponding to the weight matrix and a second horizontal summed area table corresponding to the weight image by performing horizontal summing operation on the weight matrix and the weight image; obtaining a first weighted blurring image corresponding to the weight matrix based on the first horizontal summed area table, and obtaining a second weighted blurring image corresponding to the weight image based on the second horizontal summed area table; and obtaining a pixel value of the pixel in a target image based on the first and second weighted blurring images.

    METHOD AND SYSTEM FOR TRACKING OBJECT BY AGGREGATION NETWORK BASED ON HYBRID CONVOLUTION AND SELF-ATTENTION

    公开(公告)号:US20240104772A1

    公开(公告)日:2024-03-28

    申请号:US18356315

    申请日:2023-07-21

    发明人: Jun WANG Peng YIN

    IPC分类号: G06T7/73

    摘要: Corresponding template features and search features are obtained by convolution operation, and respectively used as input features in aggregation modules. Intermediate features are obtained by performing convolution operation on the input features. The aggregation modules share the same convolution operation, and hybrid convolution in the aggregation module uses a depthwise convolution and a pointwise convolution to separate mixture between space and channel of the intermediate features. Redundancy in spatial and channel features is reduced while increasing receptive field. Self-attention module in the aggregation module learns intermediate features, and adaptively focuses on different regions to capture more global correlations. Output features of the hybrid convolution are added to output features of the self-attention module to pass through a drop-out layer to obtain final output features. The output features aggregate local and global context information. Overfitting of network is alleviated during training, thereby improving generalization ability of tracker.