Robust Registration of Remote Sensing Image Based on SURF and KCCA

Yan, Weidong; She, Huajun; Yuan, Zhanbin

doi:10.1007/s12524-013-0324-x

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…In this section, we compare the proposed method with CCA-I [6], RANSAC [7], ORSA [9] and the mode-seeking method [5] on three different image pairs with more mismatches. The threshold for SIFT initial matching is 0.8 in these experiments.…”

Section: Resultsmentioning

confidence: 99%

“…So a convenient mode-seeking algorithm which exploits the scale, orientation, and position information of SIFT features was presented in [5]. More recently, Yan et al [6] fit a line using the first pair of (kernel) CCA features and remove mismatches by thresholding the distances from points to the line. We will refer the method as CCA-I.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we proposed a mismatches removal method following CCA-I [6] with the aim of detecting as many mismatches as possible while keeping as many correct matches as possible. The method improves CCA-I in two aspects.…”

Section: Introductionmentioning

confidence: 99%

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Image Registration Using SIFT and Canonical Correlation Analysis

Zhao¹,

Tian

Li-juan³

et al. 2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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To improve the quality of SIFT feature matching, a mismatches removal method is proposed based on the collinearity property of the canonical correlation features. The influence of each match on the collineartiy degree is analyzed to find false matches. Firstly, a putative set of matches is obtained based on distances between SIFT feature descriptors. Secondly, the spatial relationship of matched points is encoded by canonical correlation analysis. The match with the largest effect on the collinearity degree is iteratively eliminated. Experimental results show the proposed method can reserve a high rate of correct matches determined by a threshold. Compared with several other mismatches removal methods, it provides comparable or better results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Registration Using SIFT and Canonical Correlation Analysis

Zhao¹,

Tian

Li-juan³

et al. 2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

Self Cite

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show abstract

“…It has certain stability in terms of the change of view and affine transformation; however, the matching speed for this algorithm is the main limitation. The speeded-up robust features (SURF) algorithm can be used to extract the feature points of the image [17][18][19][20] and implement image matching according to the correlation. However, this algorithm relies too much on the gradient direction of the pixels in the local area, which yields unsatisfactory feature matching results.…”

Section: Introductionmentioning

confidence: 99%

3-D Point Cloud Registration Algorithm Based on Greedy Projection Triangulation

2018

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To address the registration problem in current machine vision, a new three-dimensional (3-D) point cloud registration algorithm that combines fast point feature histograms (FPFH) and greedy projection triangulation is proposed. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation. Thereafter, the sample consensus initial alignment method is applied for initial transformation to implement initial registration. By adjusting the initial attitude between the two cloud points, the improved initial registration values can be obtained. Finally, the iterative closest point method is used to obtain a precise conversion relationship; thus, accurate registration is completed. Specific registration experiments on simple target objects and complex target objects have been performed. The registration speed increased by 1.1% and the registration accuracy increased by 27.3% to 50% in the experiment on target object. The experimental results show that the accuracy and speed of registration have been improved and the efficient registration of the target object has successfully been performed using the greedy projection triangulation, which significantly improves the efficiency of matching feature points in machine vision.

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“…To solve this one‐to‐many feature association problem in SURF algorithm [11, 19–26], super‐SURF image geometrical registration algorithm is proposed in this paper, in which information richness areas are selected to detect feature points and to implement feature points association. The degree of closeness of multiple feature points from the one‐to‐many feature point pairs is analysed in order to remove feature point pairs with larger errors and retain those with smaller errors.…”

Section: Introductionmentioning

confidence: 99%