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Image registration method based on graph convolutional neural network

A convolutional neural network and image registration technology, applied in the field of computer vision, can solve problems such as the inability to adopt a neighbor strategy, achieve advanced performance and improve matching accuracy

Pending Publication Date: 2021-01-01
MINJIANG UNIV
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Problems solved by technology

However, there is still no scheme to use graph convolutional neural network (GCN) for image registration. First, because there is no effective graph convolution for image registration, and second, because of non-rigid transformation, it is impossible to take effective neighbor strategy

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  • Image registration method based on graph convolutional neural network
  • Image registration method based on graph convolutional neural network

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Embodiment Construction

[0031]The present invention will be further described below in conjunction with the drawings and embodiments.

[0032]It should be pointed out that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0033]It should be noted that the terms used here are only for describing specific implementations, and are not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate There are features, steps, operations, devices, components, ...

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Abstract

The invention relates to an image registration method based on a graph convolutional neural network, and the method comprises the steps: obtaining key points of an image pair, and initializing key point matching; inputting the initial matching pairs into a multilayer perceptron to obtain feature point information of each matching pair; inputting the initial matching pairs into a graph convolutional neural network to obtain local graph spatial feature information of each matching point pair; combining the feature point information of the matching point pair with the spatial feature informationof the local graph, inputting the combined information into a multi-layer perceptron to learn a combined feature, and outputting a final feature; and calculating a loss value by utilizing the output final characteristics, and adjusting network parameters by adopting a back propagation algorithm. According to the invention, the registration precision is effectively improved.

Description

Technical field[0001]The invention relates to the technical field of computer vision, in particular to an image registration method based on graph convolutional neural network.Background technique[0002]More and more computer vision products are integrated into our daily lives, and the complex data in real life requires more and more computer vision algorithms. Estimating the geometric relationship between two images is a basic problem of computer vision neighborhood, and it plays an important role in Structure from Motion and Simultaneous Localization and Mapping. In image registration, traditional outlier removal algorithms such as RANSAC (Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM.1981Jun 1; 24(6): 381-95.) is the standard algorithm and the most popular outlier removal algorithm. GMS(Bian J, Lin WY, Matsushita Y, Yeung SK, Nguyen TD, Cheng MM.Gms: Grid-based ...

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Application Information

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IPC IPC(8): G06T7/33G06N3/04G06N3/08
CPCG06T7/33G06N3/084G06T2207/10004G06N3/045Y02T10/40
Inventor 肖国宝郑伟钟振刘鑫
Owner MINJIANG UNIV
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