Abstract
A new edge detection method based on histogram of oriented gradient (HOG)-index dictionary learning is proposed. The HOG-index dictionary includes HOG feature bases of various granularity ore image and its corresponding ground-truth binary image bases. For each pending image, its HOG feature is extracted to compare with the HOG feature bases in the dictionary. The binary image bases of the closest matched HOG bases will be chosen as a reconstruction of the original pending image. Compared to bi-neighbourhood Otsu thresholding method, experimental results show that the proposed algorithm improves both precision and noise immunity performance efficiently, especially for small particle size and large-complex ore images.
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