Stereo matching algorithm of workpiece images based on improved shape context

FAN Ying; BAI Ruilin; WANG Xiuping; LI Xin

doi:10.7510/jgjs.issn.1001-3806.2016.06.009

LASER TECHNOLOGY > 2016 > 40(6): 814-819. > DOI: 10.7510/jgjs.issn.1001-3806.2016.06.009

FAN Ying, BAI Ruilin, WANG Xiuping, LI Xin. Stereo matching algorithm of workpiece images based on improved shape context[J]. LASER TECHNOLOGY, 2016, 40(6): 814-819. DOI: 10.7510/jgjs.issn.1001-3806.2016.06.009

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Stereo matching algorithm of workpiece images based on improved shape context

1.
Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University, Wuxi 214122, China;
2.
Xinje Electronic Co. Ltd, Wuxi 214072, China

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Received Date: October 13, 2015
Revised Date: October 28, 2015
Published Date: November 24, 2016

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Abstract

Abstract

In traditional shape context, the calculation complexity was high in traversing the contour sampling points to calculate the histogram similarity and it can not meet the industrial real-time requirements. In order to solve the problem, a fast stereo matching algorithm of workpiece images based on the improved shape context was proposed. In the disparity constraints of stereo image centroid, by using the histogram distribution of shape context, candidate matching points set were obtained and the calculation complexity was greatly reduced. To increase the discrimination between the matching points and non-matching points, the similarity measurement of shape context was weighted by the weighted coefficient. During the matching period, the improved shape context was combined with the histograms of oriented gradients of the corresponding contour feature points in 33 neighborhood to acquire the initial matching point set. At last, the false matching points were eliminated by random sample consensus algorithm. Theoretical analysis and experimental verification were applied to workpiece images, and the corresponding experimental comparison was presented. The experiment results show that this improved algorithm has higher precision, faster matching speed and high robustness.
- image processing,
- stereo matching,
- shape context,
- disparity constraints,
- histograms of oriented gradients,
- random sample consensus algorithm

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References

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