LoG, DoG, and DoH Feature Detection
LoG is an acronym standing for Laplacian of Gaussian, DoG is an acronym standing for Difference of Gaussians (DoG is an approximation of LoG), and DoH is an acronym standing for Determinant of the Hessian.
These detectors are more completely described in blob detection, however the LoG and DoG blobs do not necessarily make highly selective features, since these operators may also respond to edges. To improve the corner detection ability of the DoG detector, the feature detector used in the SIFT system uses an additional post-processing stage, where the eigenvalues of the Hessian of the image at the detection scale are examined in a similar way as in the Harris operator. If the ratio of the eigenvalues is too high, then the local image is regarded as too edge-like, so the feature is rejected. The DoH operator on the other hand only responds when there are significant grey-level variations in two directions.
Read more about this topic: Corner Detection
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