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

Katramados, I.; Breckon, T.P.

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


Authors

I. Katramados



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). Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications. In Proc. Int. Conf. on Image Processing (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 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

Files

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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.





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