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Unmanned aerial vehicle aerial image matching method based on vocabulary tree blocking and clustering

A vocabulary tree and image technology, which is applied in the field of UAV aerial image matching based on vocabulary tree block clustering, can solve the problems of slow matching speed and large matching error, and achieve the effect of fast matching and less computing cost

Active Publication Date: 2014-12-17
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to solve the disadvantages of slow matching speed and large matching error in the matching of such images, the present invention proposes a method for matching UAV aerial images based on vocabulary tree block clustering

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  • Unmanned aerial vehicle aerial image matching method based on vocabulary tree blocking and clustering
  • Unmanned aerial vehicle aerial image matching method based on vocabulary tree blocking and clustering
  • Unmanned aerial vehicle aerial image matching method based on vocabulary tree blocking and clustering

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Embodiment Construction

[0023] The present invention will now be further described in conjunction with examples:

[0024] 1. Selection strategy of image set to be matched based on vocabulary tree

[0025] Establish a training set for a large number of images collected by the drone, establish an independent ID for each image, and extract the SIFT features of the image, so far we can get a feature set Feat={feat i } And the image ID set containing the feature, namely {ID i }, using K-Means clustering method to cluster the feature set hierarchically. The number of cluster categories is limited to k, and all the features are divided into k categories in the first layer to obtain the cluster center C i , And then repeat the above clustering process for each category. Limit the level of the clustering tree to L level, and the number of nodes in the number is Here we have implemented an unsupervised training process on the massive data collected by the drone.

[0026] In order to ensure rapidity, it is necessary...

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Abstract

The invention relates to an unmanned aerial vehicle aerial image matching method based on vocabulary tree blocking and clustering. Firstly, images in a scene are quantified by using a vocabulary tree, and hierarchical clustering is established for the centralized mass features of the images, so that rapid similarity screening of a to-be-matched image set and a mass image set is realized, a rapid scene classifying process is realized, and frame-after-frame matching and selection of the to-be-matched image set according to a traditional method are avoided. Secondly, a thumbnail of two frames of obtained images having similarity is established, and rough matching is performed on the images under the thumbnail. Then, image blocking is performed by using a clustering method, which is an effective trial for the thought of rough-to-fine matching. Furthermore, the unmanned aerial vehicle aerial image matching method is provided for the first time specific to the data characteristics of large data size of unmanned aerial vehicle aerial images, high image resolution, low image overlapping ratio and the like, so that the unmanned aerial vehicle aerial image matching accuracy and efficiency are effectively increased. The effectiveness of the method is verified by testing aerial images in a PAMView: Providence Aerial MultiView Dataset database.

Description

Technical field [0001] The invention relates to a method for matching massive aerial images, in particular to a method for unmanned aerial image matching based on vocabulary tree block clustering. Background technique [0002] Image matching is an important issue in the field of computer vision and scene analysis, and it has a wide range of applications in the fields of image stitching and 3D reconstruction. The existing image matching algorithms mainly include: the nearest neighbor matching method based on kd-tree, the approximate matching method based on image blocks, and the nearby element retrieval method based on hash table. The document "Computing Nearest-Neighbor Fields via Propagation-Assisted KD-Trees, 2012 CVPR" proposes a KD-Trees matching method based on the propagation method to calculate the dense matching relationship between two perspectives, aiming to pass one image in two images Restore the information of another image. The traditional matching method based on...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06T7/00
CPCG06V20/13G06F18/22
Inventor 张艳宁杨涛宋征玺
Owner NORTHWESTERN POLYTECHNICAL UNIV
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