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|1. (WO2018023734) SIGNIFICANCE TESTING METHOD FOR 3D IMAGE|
|Title:||SIGNIFICANCE TESTING METHOD FOR 3D IMAGE|
Disclosed is a significance testing method for a 3D image, comprising steps of: (1) respectively extracting depth characteristic vectors of a color image and a depth image on the basis of a convolutional neural network; (2) respectively generating significance maps of the depth image and the color image according to a three-layer neural network and the extracted depth characteristic vectors of the color image and the depth image; (3) linearly blending the significance maps of the color image and the depth image to obtain a significance map of a 3D image. According to the present invention, depth study characteristics of a color image and a depth image are extracted in a multi-scale area on the basis of a CNN model; a significance map of the depth image (or the color image) is generated using a trained NN model on the basis of a depth characteristic vector and a significance label of the area, the NN model being used as a classifier in this case; with the depth significance map and a color significance map as an input, a final significance map of a 3D image is generated using a linear blending method. The present testing method has advantages of small error and high precision.