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Proceedings Paper

Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement
Author(s): Amir Badshah; Aadil Jaleel Choudhry; Shan Ullah
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Paper Abstract

Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse image processing algorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-image processing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.

Paper Details

Date Published: 14 May 2017
PDF: 6 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 1033802 (14 May 2017); doi: 10.1117/12.2266941
Show Author Affiliations
Amir Badshah, International Islamic Univ. (Pakistan)
Aadil Jaleel Choudhry, National Univ. of Sciences and Technology (Pakistan)
Shan Ullah, National Univ. of Sciences and Technology (Pakistan)

Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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