OBJECT RECOGNITION USING MULTIPLE THRESHOLDING AND LOCAL BINARY SHAPE FEATURES

Sameer Singh, Tom Warsop

Abstract

Traditionally, image thresholding is applied to segmentation - allowing foreground objects to be segemented. However, selection of thresholds in such schemes can prove difficult. We propose a solution by applying multiple thresholds. The task of object recognition then becomes that of matching binary objects, for which we present a new method based on local shape features. We embed our recognition method in a system which reduces the computational increase caused by using multiple thresholding. Experimental results show our method and system work well despite only using a single example of each object class for matching.

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Paper Citation


in Harvard Style

Singh S. and Warsop T. (2009). OBJECT RECOGNITION USING MULTIPLE THRESHOLDING AND LOCAL BINARY SHAPE FEATURES . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 389-392. DOI: 10.5220/0001770903890392


in Bibtex Style

@conference{visapp09,
author={Sameer Singh and Tom Warsop},
title={OBJECT RECOGNITION USING MULTIPLE THRESHOLDING AND LOCAL BINARY SHAPE FEATURES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={389-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001770903890392},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - OBJECT RECOGNITION USING MULTIPLE THRESHOLDING AND LOCAL BINARY SHAPE FEATURES
SN - 978-989-8111-69-2
AU - Singh S.
AU - Warsop T.
PY - 2009
SP - 389
EP - 392
DO - 10.5220/0001770903890392