Dense gradient-based features (DEGRAF) for computationally efficient and invariant feature extraction in real-time applications
Ioannis Katramados, Toby P. Breckon · 2016
We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computationally expensive dense scene sensing, we show that Dense Gradient-based Features (DeGraF) can be derived based on initial multi-scale division of Gaussian preprocessing, weighted centroid gradient calculation and either local saliency (DeGraF-α) or signal-to-noise inspired (DeGraF-β) final stage filtering. We present two variants (DeGraF-α / DeGraF-β) of which the signal-to-noise based approach is shown to perform admirably against the state of the art in terms of feature density, computational efficiency and feature stability. Our approach is evaluated under a range of environmental conditions typical of automotive sensing applications with strong feature density requirements.