Scale-invariant feature transform

Scale-invariant feature transform (SIFT) descriptors provide an alternative representation for image regions. They are very useful for matching images. As demonstrated earlier, simple corner detectors work well when the images to be matched are similar in nature (with respect to scale, orientation, and so on). But if they have different scales and rotations, the SIFT descriptors are needed to be used to match them. SIFT is not only just scale invariant, but it still obtains good results when rotation, illumination, and viewpoints of the images change as well. 

 

 

Let's discuss the primary steps involved in the SIFT algorithm that transforms image content into local feature coordinates that are invariant to translation, rotation, scale, and other imaging parameters.

Algorithm to compute SIFT descriptors

• Scale-space extrema detection: Search over multiple scales and image locations, the location and characteristic scales are given by DoG detector
• Keypoint localization...