Abstract: The present invention belongs to the technical field of mechanical fault data recognition, and discloses a cross-domain mechanical fault diagnosis method based on multi-channel feature fusion of an improved CBAM and use thereof, wherein the method comprises: conducting preliminary feature extraction in a grey-scale graph formed by original signals with convolutional neural network, obtaining high-level features, and compressing the high-level features with a full-connection layer module; conducting deep-level multi-sensor feature extraction with an improved convolutional block attention module (CBAM); conducting fusion for multi-sensor features extracted with an improved convolutional block attention module and obtaining multi-sensor fusion features; and inputting the multi-sensor fusion features into a tag assignor for fault diagnosis results.

Abstract: The present disclosure provides an interactive image matting method, a computer readable memory medium, and a computer device. The interactive image matting method includes steps: obtaining an original image; collecting foreground sample points on a hair edge foreground region of the original image and collecting background sample points on a hair edge background region of the original image by a human-computer interaction method to correspondingly obtain a foreground sample space and a background sample space; receiving a marking operation instruction input by a user, and smearing a hair region of the original image according to the marking operation instruction to mark unknown regions; traversing the unknown regions to obtain a pixel of each unknown region, traversing all the sample pairs to select a sample pair with a minimum overall cost function value for the pixel of each unknown region.

Abstract: The present disclosure provides an interactive image matting method, a computer readable memory medium, and a computer device. The interactive image matting method includes steps: obtaining an original image; collecting foreground sample points on a hair edge foreground region of the original image and collecting background sample points on a hair edge background region of the original image by a human-computer interaction method to correspondingly obtain a foreground sample space and a background sample space; receiving a marking operation instruction input by a user, and smearing a hair region of the original image according to the marking operation instruction to mark unknown regions; traversing the unknown regions to obtain a pixel of each unknown region, traversing all the sample pairs to select a sample pair with a minimum overall cost function value for the pixel of each unknown region.