Automated image registration using the projective transformation model and block matching feature point pair selection

1999 ◽  
Author(s):  
Richard R. Schultz ◽  
Mark G. Alford
Keyword(s):  
Image Registration ◽  
Projective Transformation ◽  
Block Matching ◽  
Feature Point ◽  
Transformation Model ◽  
Point Pair ◽  
Automated Image Registration

Image registration based on criteria of feature point pair mutual information

2011 ◽  
Vol 5 (6) ◽  
pp. 560 ◽  
Author(s):  
G. Lu ◽  
J. Yan ◽  
Y. Kou ◽  
J. Zhang
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Feature Point ◽  
Point Pair

Automated image registration by maximization of a region similarity metric

1997 ◽  
Vol 8 (6) ◽  
pp. 513-518 ◽  
Author(s):  
Zhi-Pei Liang ◽  
Hao Pan ◽  
Richard L. Magin ◽  
Narendra Ahuja ◽  
Thomas S. Huang
Keyword(s):  
Image Registration ◽  
Similarity Metric ◽  
Automated Image Registration

Image registration based on feature point sets

2003 ◽  
Author(s):  
Quanbing Zhang ◽  
Bin Luo ◽  
Sui Wei ◽  
Shangjun Yang
Keyword(s):  
Image Registration ◽  
Feature Point ◽  
Point Sets

scholarly journals Automated Image Registration: II. Intersubject Validation of Linear and Nonlinear Models

1998 ◽  
Vol 22 (1) ◽  
pp. 153-165 ◽  
Author(s):  
Roger P. Woods ◽  
Scott T. Grafton ◽  
John D. G. Watson ◽  
Nancy L. Sicotte ◽  
John C. Mazziotta
Keyword(s):  
Image Registration ◽  
Nonlinear Models ◽  
Linear And Nonlinear ◽  
Automated Image Registration

scholarly journals A Robust False Matching Points Detection Method for Remote Sensing Image Registration

2015 ◽  
Vol XL-7/W3 ◽  
pp. 699-702
Author(s):  
X. J. Shan ◽  
P. Tang
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Detection Method ◽  
Graph Transformation ◽  
Remote Sensing Image ◽  
Transformation Model ◽  
Superior Performance ◽  
Geometric Distortion ◽  
Remote Sensing Images ◽  
Accuracy Result

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

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