  Estimates of image gradients play a ubiquitous role in image segmentation and
classification problems since gradients directly relate to the boundaries or
the edges of a scene. This paper proposes an unified approach to gradient
estimation based on fractional calculus that is computationally cheap and
readily applicable to any existing algorithm that relies on image gradients. We
show experiments on edge detection and image segmentation on the Stanford
Backgrounds Dataset where these improved local gradients outperforms state of
the art, achieving a performance of 79.2% average accuracy.
