METHOD AND SYSTEM FOR SELECTING IMAGE REGION THAT FACILITATES BLUR KERNEL ESTIMATION

    公开(公告)号:US20210192251A1

    公开(公告)日:2021-06-24

    申请号:US16079565

    申请日:2018-01-08

    Abstract: The present invention discloses a method and system for selecting an image region that facilitates blur kernel estimation, in which the method includes: calculating a relative total variation value of each pixel in a blurred image to obtain a relative total variation mapping image; setting a threshold value to determine whether respective pixel in the image is a boundary pixel or not; then sampling the blurred image and its relative total variation mapping image to obtain a series of image patches; and finally counting the number of boundary pixels in each mapping image patch and selecting out an image region that facilitates blur kernel estimation. According to the method and the system, the problems of excessive dependency on operator experience, low efficiency and the like in the existing region selection methods are effectively solved. The image region that facilitates blur kernel estimation is automatically selected out. And the method and the system are suitable for the application occasion of the blur kernel estimation in an image deblurring algorithm.

    SPATIOTEMPORAL ACTION DETECTION METHOD

    公开(公告)号:US20210248378A1

    公开(公告)日:2021-08-12

    申请号:US16965015

    申请日:2020-01-07

    Abstract: A spatiotemporal action detection method includes performing object detection on all frames of a sample video to obtain a candidate object set; calculating all interframe optical flow information on the sample video to obtain a motion set; constructing a spatiotemporal convolution-deconvolution network of an attention mechanism and a motion attention mechanism of an additional object; adding both a corresponding sparse variable and a sparse constraint to obtain a network structure S after performing spatiotemporal convolution processing on each time segment of the sample video; training the network structure S with an objective function based on classification loss and loss of the sparse constraint of cross entropy; and calculating an action category and a sparse coefficient corresponding to each time segment of a test sampled video to obtain an object action spatiotemporal location.

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