In computer vision and biological vision, scaling transformations arise because of the perspective image mapping and because of objects having different physical size in the world. In these areas, scale invariance refers to local image descriptors or visual representations of the image data that remain invariant when the local scale in the image domain is changed. Detecting local maxima over scales of normalized derivative responses provides a general framework for obtaining scale invariance from image data. Examples of applications include blob detection, corner detection, ridge detection, and object recognition via the scale-invariant feature transform.