Contextual Saliency for Nonrigid Landmark Registration and Recognition of Natural Patterns

Luke Palmer, Tilo Burghardt

Abstract

In this paper we develop a method for injecting within-pattern information into the matching of point patterns through utilising the shape context descriptor in a novel manner. In the domain of visual animal biometrics, landmark distributions on animal coats are commonly used as characteristic features in the pursuit of individual identification and are often derived by imaging surface entities such as bifurcations in scales, fur colouring, or skin ridge minutiae. However, many natural distributions of landmarks are quasiregular, a property with which state-of-the-art registration algorithms have difficulty. The method presented here addresses the issue by guiding matching along the most distinctive points within a set based on a measure we term contextual saliency. Experiments on synthetic data are reported which show the contextual saliency measure to be tolerant of many point-set transformations and predictive of correct correspondence. A general point-matching algorithm is then developed which combines contextual saliency information with naturalistic structural constraints in the form of the thin-plate spline. When incorporated as part of a recognition system, the presented algorithm is shown to outperform two widely used point-matching algorithms on a real-world manta ray data set.

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Paper Citation


in Harvard Style

Palmer L. and Burghardt T. (2015). Contextual Saliency for Nonrigid Landmark Registration and Recognition of Natural Patterns . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 403-410. DOI: 10.5220/0005268604030410


in Bibtex Style

@conference{visapp15,
author={Luke Palmer and Tilo Burghardt},
title={Contextual Saliency for Nonrigid Landmark Registration and Recognition of Natural Patterns},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005268604030410},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Contextual Saliency for Nonrigid Landmark Registration and Recognition of Natural Patterns
SN - 978-989-758-089-5
AU - Palmer L.
AU - Burghardt T.
PY - 2015
SP - 403
EP - 410
DO - 10.5220/0005268604030410