Howtosignalprocessing
- What is scale space extrema detection?
- How does sift feature detection work?
- How is SIFT invariant to scale?
- How does SIFT satisfy the rotation invariant property?
What is scale space extrema detection?
Scale-Space Extrema Detection
To detect the local maxima and minima of D(x, y, σ) each point is compared with its 8 neighbours at the same scale, and its 9 neighbours up and down one scale. If this value is the minimum or maximum of all these points then this point is an extrema.
How does sift feature detection work?
SIFT helps locate the local features in an image, commonly known as the 'keypoints' of the image. These keypoints are scale & rotation invariant that can be used for various computer vision applications, like image matching, object detection, scene detection, etc.
How is SIFT invariant to scale?
Scale-invariant feature transform (SIFT) is a broadly adopted feature extraction method in image classification tasks. The feature is invariant to scale and orientation of images and robust to illumination fluctuations, noise, partial occlusion, and minor viewpoint changes in the images.
How does SIFT satisfy the rotation invariant property?
SIFT features are scale and rotation invariant, and hence robust to substantial range of affine distortion, change in viewpoint, illumination and noise. Both spatial and frequency localization of the features reduces the effect of occlusion, clutter, or noise.
