Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications
Katramados, I.; Breckon, T.P.
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.
Katramados, I., & Breckon, T. (2016). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. In 2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings (300-304). https://doi.org/10.1109/icip.2016.7532367
|Conference Name||2016 IEEE International Conference on Image Processing.|
|Conference Location||Phoenix, AZ, USA|
|Start Date||Sep 25, 2016|
|End Date||Sep 28, 2016|
|Acceptance Date||Jul 12, 2016|
|Online Publication Date||Aug 19, 2016|
|Publication Date||Aug 19, 2016|
|Deposit Date||Oct 3, 2016|
|Publicly Available Date||Oct 3, 2016|
|Book Title||2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, Arizona, USA ; proceedings.|
|Related Public URLs||http://community.dur.ac.uk/toby.breckon/publications/papers/katramados16degraf.pdf|
Accepted Conference Proceeding
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