# DAVIDSTUTZ

Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
09thMAY2016

First, given a specific scale, the orientation of the interest point is determined by computing a gradient orientation histogram under a Gaussian window and using the maximum bin as orientation. In practice, $36$ bins are used to determine the orientation and up to three orientations corresponding to bins above 80% of the maximum bin are retained as additional orientations. Then, SIFT divides a rectangular region, rotated according to the orientation determined previously, around the interest point into a $4 \times 4$ grid. For each grid element, a 8-bin gradient orientation histogram is computed using trilinear interpolation. Pixels are weighted according to gradient magnitude and a Gaussian window. This results in a $c = 4\cdot 4\cdot 8$-dimensional descriptors which is $L_2$-normalized.