For example, check a simple image below. A corner in a small image within a small window is flat when it is zoomed in the same window. So Harris corner is not scale invariant.
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So, in , D. This paper is easy to understand and considered to be best material available on SIFT. So this explanation is just a short summary of this paper. It is OK with small corner.
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But to detect larger corners we need larger windows. For this, scale-space filtering is used. In it, Laplacian of Gaussian is found for the image with various values. LoG acts as a blob detector which detects blobs in various sizes due to change in.
In short, acts as a scaling parameter. For eg, in the above image, gaussian kernel with low gives high value for small corner while guassian kernel with high fits well for larger corner. So, we can find the local maxima across the scale and space which gives us a list of values which means there is a potential keypoint at x,y at scale.
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Difference of Gaussian is obtained as the difference of Gaussian blurring of an image with two different , let it be and. This process is done for different octaves of the image in Gaussian Pyramid. It is represented in below image:. Once this DoG are found, images are searched for local extrema over scale and space.
sift | Definition of sift in English by Oxford Dictionaries
For eg, one pixel in an image is compared with its 8 neighbours as well as 9 pixels in next scale and 9 pixels in previous scales. If it is a local extrema, it is a potential keypoint. It basically means that keypoint is best represented in that scale. It is shown in below image:.
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Once potential keypoints locations are found, they have to be refined to get more accurate results. They used Taylor series expansion of scale space to get more accurate location of extrema, and if the intensity at this extrema is less than a threshold value 0.
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