A Defiled Image Segmentation Method Integrating Deep Neural Network and CV Model

Abstract

The invention discloses a stained image segmentation method integrating a deep neural network and a CV model, which belongs to the technical field of image processing and solves the problems of using the existing neural network training samples for image segmentation and the unsupervised nature of the CV model. The technical solution is: constructing a random contamination model, loading the image data of the contamination image, constructing a contamination image fusion segmentation model, extracting the characteristics of the contamination image, and using the energy function of the CV model to complete the error back propagation to update the network parameters; After multiple rounds of iterative learning, the defiled image fusion segmentation model is used to complete the segmentation of the defiled images; the present invention integrates the unsupervised characteristics of the CV model into the neural network, so that the neural network can obtain accurate images with a small number of training samples. Mapping relationship, applied to the segmentation of defiled images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a stained image segmentation method integrating a deep neural network and a CV model. Background technique [0002] With the continuous development of science and technology, people's requirements for the accuracy of image processing continue to increase, and higher requirements are also put forward for the processing of large samples of high-complexity images. The field of image processing has ushered in new challenges. Image segmentation is one of the most basic and critical steps in image processing. It provides basic discriminative data for further image processing (recognition, understanding, etc.) bridge. Image segmentation is a basic subject in the field of image understanding, such as target detection, target recognition, and object tracking. The errors generated in the segmentation will be passed up to the high-level image analysis and understanding...

AI Technical Summary

Problems solved by technology

[0005] The present invention overcomes the deficiencies of the prior art, provides a method for segmenting defaced images that integrates deep neural networks and CV models, and solves the problem of large demand for training samples using existing neural networks for image segmentation and the unsupervised nature of CV models The problem

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