Sequenced Image Segmentation Method of Ceramic Material Parts with Improved Fully Convolutional Neural Network

Abstract

The present invention proposes an improved full convolutional neural network sequence image segmentation method for ceramic material parts, including steps: S10: manually labeling the collected original images, distinguishing objects and backgrounds with different categories, and obtaining training labels, Use the index mode to represent the label map of the training samples; S20: construct an improved network model based on the full convolutional neural network, and perform training; S30: calculate the loss function and backpropagation calculation loss function according to the gradient descent algorithm, and train the network Learning, the learning rate is reduced to a factor of 10 when the validation accuracy stops increasing. The fully convolutional neural network is an improved structure based on the convolutional neural network. On the basis of maintaining the good classification performance of CNN, it better maintains the spatial position relationship between pixel matrices, which is more conducive to global feature extraction, and can comprehensively Learning the visual characteristics of the object, it has good anti-interference ability, and can automatically separate the target object from the background to realize 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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