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An image patch matching method based on multi-scale convolution

A matching method and multi-scale technology, applied in the field of image processing, can solve the problem of not considering the multi-scale features of the patch, and achieve the effect of discriminative and invariant, good matching performance, and improved matching performance

Active Publication Date: 2022-02-11
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

But this method does not consider the multi-scale features of the patch

Method used

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  • An image patch matching method based on multi-scale convolution
  • An image patch matching method based on multi-scale convolution
  • An image patch matching method based on multi-scale convolution

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Experimental program
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Embodiment

[0084] S1. To evaluate the performance of our method, we validate it on the widely used homogeneous dataset UBC PhotoTour and the heterogeneous dataset VIS-NIR. UBC PhotoTour contains three subsets: Liberty, Yosemite, and Notredame. The three subsets contain 450K, 634K, and 468K independent patch blocks and 160K, 230K, and 147K unique 3D points. VIS-NIR contains 9 subsets, namely: Country, Field, Forest, Indoor, Mountain, Oldbuilding, Street, Urban, Water, and the matching samples and non-matching samples in each subset account for half. On the UBC PhotoTour dataset, we train on Liberty, Yosemite, and Notredame respectively, and then test on the other two subsets. On the VIS-NIR dataset, we train on the Country subset and test on the other 8 subsets;

[0085] S2. Each independent 3D point on the UBC PhotoTour dataset contains 2-5 patches, and 2 patches with the same 3D point form a matching pair. Randomly select 2 patches for each 3D point on each subset of UBC PhotoTour (T ...

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Abstract

The invention discloses an image patch matching method based on multi-scale convolution, by preparing a data set; making a data set; data preprocessing; designing a three-branch-double-channel network structure; designing a multi-scale convolution module; according to the extracted features Compute similarity; network training; predict matching probabilities; evaluate network performance. The invention effectively overcomes the problems of insufficient training and non-use of multi-scale information in the prior art, greatly improves the performance of the network, improves the training efficiency of the network, and enhances the robustness of the network. The invention can be applied to the fields of image registration, image retrieval, image tracking, multi-view reconstruction and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image patch matching method based on multi-scale convolution. Background technique [0002] Establishing an accurate matching correspondence between image patches plays a vital role in many computer vision fields, such as: image registration, image retrieval, fine-grained classification, etc. Since the appearance of the image is easily affected by many aspects such as viewing angle changes, illumination changes, occlusion, and camera parameter settings, image matching is very challenging, and the extracted features need to have good invariance and discrimination. In this paper we propose a general approach that not only achieves better matching results on homogeneous datasets, but also achieves state-of-the-art performance on more difficult heterogeneous datasets. [0003] Before deep learning, SIFT-based methods were widely used in the field of image mat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06V10/46
CPCG06V10/462G06V10/7557G06F18/214
Inventor 王爽焦李成魏少玮方帅杨博武李彦锋权豆
Owner XIDIAN UNIV