Feature Detection
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Feature definitions vary but essentially a salient part of the image or data
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Some also have a notion of a Feature descriptor or feature vector
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SIFT is a very popular example that uses many techniques
SIFT Feature Extraction Overview
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Scale-space extrema detection: The first stage of computation searches over
all scales and image locations. It is implemented efficiently by using a
difference-of-Gaussian function to identify potential interest points that
are invariant to scale and orientation.
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Keypoint localization: At each candidate location, a detailed model is fit to
determine location and scale. Keypoints are selected based on measures of
their stability.
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Orientation assignment: One or more orientations are assigned to each
keypoint location based on local image gradient directions. All future
operations are performed on image data that has been transformed relative to
the assigned orientation, scale, and location for each feature, thereby
providing invariance to these transformations.
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Keypoint descriptor: The local image gradients are measured at the selected
scale in the region around each keypoint. These are transformed in to a
representation that allows for significant levels of local shape distortion
and change in illumination