Towards real-time object recognition using pairs of lines

MEIKLE, S., AMAVASAI, B. P. and CAPARRELLI, Fabio (2005). Towards real-time object recognition using pairs of lines. Real-Time Imaging, 11 (1), 31-43.

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This paper presents a description of the "Pairs Of Lines" object recognition algorithm used in the MIMAS Computer Vision toolkit. This toolkit was developed at Sheffield Hallam University and the "Pairs of Lines" method was used in a recent European Union funded project.(1) The algorithm was developed to enable a micro-robot system (Amavasai et al., InstMC Journal of Measurement and Control) to recognize geometric planar objects in real-time, in a noisy environment. The method involves using straight line segments which are extracted from both the known object models and from the visual scene that the objects are to be located in. Pairs of these straight lines are then compared. If there is a geometric match between the two pairs an estimate of the possible position, orientation and scale of the model in the scene is made. The estimates are collated, as all possible pairs of lines are compared. The process yields the position, orientation and scale of the known models in the scene. The algorithm has been optimized for speed. This paper describes the method in detail and presents experimental results which indicate that the technique exhibits robustness to camera noise and partial occlusion and produces recognition in times under 1 s on a desktop PC. Recognition times are shown to be from 2 to 16 times faster than with the well-studied pairwise geometric histograms method. Recognition rates of up to 80% were achieved with scenes having signal to noise ratios of 2.5. (c) 2005 Elsevier Ltd. All rights reserved.

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Materials Analysis and Research Services
Identification Number:
Page Range: 31-43
Depositing User: Ann Betterton
Date Deposited: 12 Jul 2010 15:12
Last Modified: 19 Mar 2021 01:00

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