Image traversal processing method, device and computing device based on scene segmentation

Abstract

The invention discloses an image traversal processing method, device, computing device and computer storage medium based on scene segmentation. The method includes: acquiring an image to be processed and time processing parameters; inputting the image to be processed into a scene segmentation network, and obtaining The scene segmentation result corresponding to the image to be processed; according to the scene segmentation result corresponding to the image to be processed, determine the contour information of the specific object; according to the contour information of the specific object and the obtained time processing parameters, add the time processing effect to obtain the processed image. This technical solution can quickly and accurately obtain the scene segmentation result corresponding to the image, and based on the scene segmentation result, it can more accurately add time processing effects to the characters, background, etc. in the image, and obtain an image with a time-traveling effect.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image traversal processing method, device, computing device and computer storage medium based on scene segmentation. Background technique [0002] In the existing technology, image scene segmentation processing methods are mainly based on fully convolutional neural networks in deep learning. These processing methods use the idea of ​​transfer learning to migrate the network obtained through pre-training on large-scale classification data sets to image processing methods. The segmentation network is trained on the segmentation data set to obtain the segmentation network for scene segmentation, and then the segmentation network is used to segment the scene of the image. [0003] The network architecture used in the segmentation network obtained in the prior art directly uses the image classification network, and the size of the convolution block in the convoluti...

AI Technical Summary

Problems solved by technology

However, for image scene segmentation, the scene often contains objects of different sizes. Using a segmentation network with a fixed-size receptive field often causes problems when dealing with too large and too small objects. For example, for smaller objects, the receptive field It will capture too much background around the target, thereby confusing the target with the background, causing the target to be missed and misjudged as the background; for larger targets, the receptive field can only capture a part of the target, which makes the target category judgment biased, resulting in Discontinuous Segmentation Results

Therefore, the image scene segmentation processing method in the prior art has the problem of low accuracy of image scene segmentation, and the obtained segmentation results cannot be used to add processing effects to the characters, backgrounds, etc. in the image, and the obtained The displayed effect of the processed image is poor

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