基于SIFT的图像配准程序 SIFT特征匹配算法是目前国内外特征点匹配研究领域的热点与难点,其匹配能力较强,可以处理两幅图像之间发生平移、旋转、仿射变换情况下的匹配问题,甚至在某种程度上对任意角度拍摄的图像也具备较为稳定的特征匹配能力-SIFT-based image registration procedure is the SIFT feature matching algorithm for matching feature points at home and abroad a hot
To detect the SIFT feature and to match and display keypoints as well. For more info, please refer to Lowe, D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 2 (2004), pp.91--110. Or see Lowe
About the Author ...............................................................................................................xiii About the Technical Reviewer .......................................................................................
Contents Preface xiii 1 Introduction 1 1.1 A Difficult Problem 1 1.2 The Human Vision System 2 1.3 Practical Applications of Computer Vision 3 1.4 The Future of Computer Vision 5 1.5 Material in This Textbook 6 1.6 Going Further with Computer Vision
An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These
image matching and recognition with local features
Correspondence
Semi-local and global geometric relations
Ransac and Hough TransformInstance-level recognition
Last time
Local invariant features(last lecture -C. Schmid)
Today
Camera geometry -re
This algorithm comes from author's project homepage and is patented.
So use this algorithm carefully when you wish to utilize for commercial purposes.
Good luck.
A technique to construct an affine invariant descr iptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed. Affine invariant SIFT descr iptor is first developed in an elliptical
