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

Authors:
Gerardo Aragon-Camarasa;J. Paul Siebert
Affiliations:
Computer Vision and Graphics Group, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow, G12 8QQ Scotland, United Kingdom;Computer Vision and Graphics Group, Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow, G12 8QQ Scotland, United Kingdom
Venue:
Pattern Recognition Letters
Year:
2010

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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.