Histograms of Oriented Gradients is a feature extraction method that can generate descriptors from images. This blog post aims at providing illustrative examples of HOG with Matlab, as well as discussing its interesting characteristics.

**HOG Person Detector Tutorial**

I have found a very nice and intuitive tutorial here. In this post, I will focus on the illustrative examples with Matlab.

**Key Idea**

The key idea of HOG is that, “local object appearance and shape can often be characterized rather well by the distribution of local intensity gradients or edge directions, even without precise knowledge of the corresponding gradient or edge positions” [1], meaning the distribution of gradients can represent the appearance of an image to some extent. The transformation from images to HOG achieves invariance to local geometric and photometric transformations by ignoring specific image details while remaining the distribution of gradients.

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