Improved ceramic material member sequence image segmentation method of fully convolutional neural network

Abstract

The invention provides an improved ceramic material member sequence image segmentation method of a fully convolutional neural network. The method comprises the steps of S10, performing manual marking on an acquired original image, classifying a target and a background by different kinds, obtaining a training label, and representing a label graph of a training sample in an index mode; S20, constructing an improved network model based on the fully convolutional neural network, and performing training; and S30, calculating a loss function and a reverse propagation calculation loss function according to a gradient reducing algorithm, and performing training learning on the network, wherein the learning rate is reduced to one tenth of the original learning rate when verification accuracy increase is stopped. The fully convolutional network is an improved structure based on a convolutional neural network. Based on keeping of good classification performance of the CNN, a spatial position relation between pixel matrixes in better kept, and global characteristic extraction is facilitated. The visual characteristic of an object can be comprehensively studied, and high interference resistance is realized. An objective target can be automatically divided from background, thereby realizing intelligent segmentation.

Description

technical field [0001] The invention relates to the technical field of computer graphics processing, in particular to a sequence image segmentation method in three-dimensional reconstruction of ceramic materials, in particular to an improved full convolutional neural network sequence image segmentation method for ceramic materials. Background technique [0002] In recent years, with the rise of e-commerce platforms and digital museums, the demand for 3D reconstruction has increased. Through 3D reconstruction technology, the real objects are fully presented on the network platform in a three-dimensional way. [0003] In the process of 3D reconstruction based on image sequences, the segmentation of sequence images is a crucial step in the whole reconstruction process, and the accuracy of segmentation directly affects the quality of the final 3D model and the accuracy of texture. However, when collecting multi-angle image series in the natural environment, the quality of the c...

AI Technical Summary

Problems solved by technology

Region-based segmentation methods often cause over-segmentation of the image, while pure edge detection methods sometimes cannot provide a better regional structure

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