Histogram of Oriented Gradients (HOG) are feature descriptors 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. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy. The following matlab project contains the source code and matlab examples used for hog.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there.
Hog (histogram of oriented gradients) mex implementation in matlab
Histogram of oriented gradients (hog) code using matlab
Broken strand detection in matlab
Change the priority of the matlab process programatically.
Fillnans replaces all nans in array using inverse-distance weighting between non-nan values. in matlab
Experimental (semi ) variogram in matlab