Publication details for Professor Toby BreckonKatramados, I. & Breckon, T.P. (2016), Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications, 2016 IEEE International Conference on Image Processing. Phoenix, AZ, USA, IEEE, Piscataway, NJ, 300-304.
- Publication type: Conference Paper
- ISSN/ISBN: 9781467399616, 2381-8549
- DOI: 10.1109/ICIP.2016.7532367
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Author(s) from Durham
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.