Scale Space - Scale Selection - Scale Invariant Feature Detection

Scale Invariant Feature Detection

Following this approach of gamma-normalized derivatives, it can be shown that different types of scale adaptive and scale invariant feature detectors can be expressed for tasks such as blob detection, corner detection, ridge detection and edge detection (see the specific articles on these topics for in-depth descriptions of how these scale-invariant feature detectors are formulated). Furthermore, the scale levels obtained from automatic scale selection can be used for determining regions of interest for subsequent affine shape adaptation to obtain affine invariant interest points or for determining scale levels for computing associated image descriptors, such as locally scale adapted N-jets.

Recent work has shown that also more complex operations, such as scale-invariant object recognition can be performed in this way, by computing local image descriptors (N-jets or local histograms of gradient directions) at scale-adapted interest points obtained from scale-space extrema of the normalized Laplacian operator (see also scale-invariant feature transform) or the determinant of the Hessian (see also SURF); see also the Scholarpedia article on the scale-invariant feature transform for a more general outlook of object recognition approaches based on receptive field responses in terms Gaussian derivative operators or approximations thereof.