Distinctive Image Features from Scale-Invariant Keypoints

"Distinctive Image Features from Scale-Invariant Keypoints," Lowe, IJCV, 2004.




This paper presents a new method for image matching, which is called Scale Invariant Feature Transform(SIFT). SIFT can extract distinctive features from image data, and the features are invariant to image rotation and scale, and robust matching across affine distortion, addition of noise, and change in illumination.

1. Detection of scale-space extrema: use difference of Gaussian(DOG) to identify the candidate keypoints.
2. Accurate keypoint localization: do keypoint selection for rejecting some unstable candidate keypoints.
3. Orientation assignment: assign an orientation to each keypoint based on local image properties for invariance to image rotation.
   
4. Local image descriptor: tranform into a representation with above parameters.
   

I think local points is an creative idea that I have learnt in retrieve system, and the accuracy of SIFT is also amazing to me. Clustering had been move on a big step since SIFT.