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I am not clear about the difference between the HOG and EOH. Hog is based on image derivatives EOH is based on edge directions. It seems that HOG also somehow a representation of EOH.
Could you please give me some explanation about how EOH differs from HOG and the advantages over EOH compare to HOG. In what circumstances we can use EOH compare to HOG?
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Edge Orientation Histogram. In terms of avoiding the amount of non important gradients that could potentially be introduced by this methodology, an option is to just take into account the edges detected by a very robust method as the canny edge detector.
The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.
HOG decomposes an image into small squared cells, computes an histogram of oriented gradients in each cell, normalizes the result using a block-wise pattern, and return a descriptor for each cell.
I think the main difference is that for a HOG, the actual gradient direction is calculated and then binned, where for an EOH the edge orientation is evaluated by searching the maximum response over a set of edge filter kernels. So you could say that HOG does the binning after the gradient computation, where EOH directly calculates the gradient in bins. Depending on the amount of bins you want, one will be faster than the other.
In an EOH, light-dark and dark-light edges are usually treated the same and the orientations are therefore in a range of 0 to pi, where in a HOG the bins usually span a full 2*pi. You can easily make an EOH do this too however.
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