Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications

Katramados, I.; Breckon, T.P.

Abstract

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.

Citation

Katramados, I., & Breckon, T. (2016, September). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. Presented at 2016 IEEE International Conference on Image Processing., Phoenix, AZ, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2016 IEEE International Conference on Image Processing.
• Start Date: Sep 25, 2016
• End Date: Sep 28, 2016
• Acceptance Date: Jul 12, 2016
• Online Publication Date: Aug 19, 2016
• Publication Date: 2016
• Deposit Date: Oct 3, 2016
• Publicly Available Date: Oct 3, 2016
• Pages: 300-304
• Series ISSN: 2381-8549
• Book Title: Proc. Int. Conf. on Image Processing
• DOI: https://doi.org/10.1109/ICIP.2016.7532367
• Keywords: dense features, feature invariance, feature points, intensity weighted centroids, automotive vision
• Public URL: https://durham-repository.worktribe.com/output/1151144
• Publisher URL: https://breckon.org/toby/publications/papers/katramados16degraf.pdf
• Related Public URLs: http://community.dur.ac.uk/toby.breckon/publications/papers/katramados16degraf.pdf