In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust Graph Transformation Matching for non-rigid registration
Image and Vision Computing
A fully affine invariant image comparison method
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Information Sciences: an International Journal
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating correspondence between arbitrarily selected points in two widely-separated views
Advanced Engineering Informatics
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Intensity-Based Image Registration by Nonparametric Local Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Computer Vision: Models, Learning, and Inference
Computer Vision: Models, Learning, and Inference
Fast multi-view segment graph kernel for object classification
Signal Processing
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This paper presents an automated image registration approach that is robust to perspective distortions. State-of-the-art method affine-SIFT uses affine transform to simulate various viewpoints to increase the robustness of registration. However, affine transformation does not follow the process by which real-world images are formed. To solve this problem, we propose a perspective scale invariant feature transform (PSIFT) that uses homographic transformation to simulate perspective distortion. As for ASIFT, PSIFT is based on the scale invariant feature transform (SIFT) and has a two-resolution scheme, namely a low-resolution phase and a high-resolution phase. The low-resolution phase of PSIFT simulates several image views following a perspective transformation by varying two camera axis orientation parameters. Given those simulated images, SIFT is then used to extract features and find matches among them. In the high-resolution phase, the perspective transformations which lead the largest number of matches in the low-resolution stage are selected to generate SIFT features on the original images. Experimental results obtained on three categories of low-altitude remote sensing images and Morel-Yu's dataset show that PSIFT outperforms significantly the state-of-the-art ASIFT, SIFT, Random Ferns, Harris-Affine, MSER and Hessian Affine, especially when images suffer severe perspective distortion.