Editing Scale-invariant feature transform (section)

=== Scale-invariant feature detection ===
Lowe's method for image feature generation transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation, partially invariant to illumination changes, and robust to local geometric distortion. These features share similar properties with neurons in the [[primary visual cortex]] that encode basic forms, color, and movement for object detection in primate vision.<ref name="Serre2005" /> Key locations are defined as maxima and minima of the result of [[difference of Gaussians]] function applied in [[scale space]] to a series of smoothed and resampled images. Low-contrast candidate points and edge response points along an edge are discarded. Dominant orientations are assigned to localized key points. These steps ensure that the key points are more stable for matching and recognition. SIFT descriptors robust to local affine distortion are then obtained by considering pixels around a radius of the key location, blurring, and resampling local image orientation planes.