Unsupervised clustering in Hough space for recognition of multiple instances of the same object in a cluttered scene

Aragon Camarasa, G. and Siebert, J.P. (2010) Unsupervised clustering in Hough space for recognition of multiple instances of the same object in a cluttered scene. Pattern Recognition Letters, 31(11), pp. 1274-1284. (doi:10.1016/j.patrec.2010.03.003)

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Publisher's URL: http://dx.doi.org/10.1016/j.patrec.2010.03.003

Abstract

We describe an active binocular vision system that is capable of localising multiple instances of objects of the same-class in different settings within a covert, pre-attentive, visual search strategy. By clustering SIFT-feature matches that have been projected into a non-quantised (i.e. continuous) Hough space we are able to detect up to 6 same-class object instances simultaneously while tolerating up to ∼ 66% of each object’s surface being occluded by another object instance of the same-class. Our findings are based on using a database of ∼ 2300 images of synthetically composited and real-world images.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Siebert, Dr Jan and Aragon Camarasa, Dr Gerardo
Authors: Aragon Camarasa, G., and Siebert, J.P.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Pattern Recognition Letters
Publisher:Elsevier BV
ISSN:0167-8655
ISSN (Online):1872-7344
Published Online:06 March 2010

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