GLOH

Feature detection
Edge detection
Canny Deriche Differential Sobel Prewitt Roberts cross
Corner detection
Harris operator Shi and Tomasi Level curve curvature Hessian feature strength measures SUSAN FAST
Blob detection
Laplacian of Gaussian (LoG) Difference of Gaussians (DoG) Determinant of Hessian (DoH) Maximally stable extremal regions PCBR
Ridge detection
Hough transform
Hough transform Generalized Hough transform
Structure tensor
Structure tensor Generalized structure tensor
Affine invariant feature detection
Affine shape adaptation Harris affine Hessian affine
Feature description
SIFT SURF GLOH HOG
Scale space
Scale-space axioms Axiomatic theory of receptive fields Implementation details Pyramids

GLOH (Gradient Location and Orientation Histogram) is a robust image descriptor that can be used in computer vision tasks. It is a SIFT-like descriptor that considers more spatial regions for the histograms. The higher dimensionality of the descriptor is reduced to 64 through principal components analysis (PCA).

See also

References

Krystian Mikolajczyk and Cordelia Schmid "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 27, pp 1615--1630, 2005.

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