Phase correlation supported feature track- matching algorithm for repeating texture

doi:10.6046/gtzyyg.2018.01.01

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Abstract

Building facade is the main content of street images captured by mobile measurement system and contains a lot of regular repeating textures. Applying feature matching algorithm to such images may cause a lot of false matches, which seriously affect the later image orientation and three-dimensional reconstruction. To solve this problem, this paper proposes a phase correlation supported KLT (Kanade-Lucas-Tomasi)feature track-matching algorithm. Firstly, phase correlation algorithm was applied from global to local scale to get crude registration. Then the KLT algorithm was used to track the corners at each matched area. The experimental results show that, when match between building dense street images, the algorithm proposed have a greater increase than pure feature matching algorithm in correct matching rate, and the distribution of features is relatively uniform, which can effectively solve the feature matching problem of street images with regular repeating textures.

Keywords street image sequences repeating texture phase correlation KLT

:	TP751.1
	P237.4

Issue Date: 08 February 2018

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	Li YAN
	Xun GONG
	Hong XIE

Cite this article:

Li YAN,Xun GONG,Hong XIE. Phase correlation supported feature track- matching algorithm for repeating texture[J]. Remote Sensing for Land & Resources, 2018, 30(1): 1-6.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.01 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/1

Fig.1 Technical flowchart of algorithm proposed in this paper

Fig.2 Registration results of block phase correlation

Fig.3 Comparison of matching results using SIFT and algorithm proposed in this paper

Tab.1 Statistics of matching results using SIFT and algorithm proposed in this paper(个)

Tab.2 Statistics of matching time by SIFT and algorithm proposed in this paper(s)

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