14 May 2017 Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement
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Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 1033802 (2017) https://doi.org/10.1117/12.2266941
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan
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.
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Amir Badshah, Amir Badshah, Aadil Jaleel Choudhry, Aadil Jaleel Choudhry, Shan Ullah, Shan Ullah, } "Robust feature estimation by non-rigid hierarchical image registration and its application in disparity measurement", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 1033802 (14 May 2017); doi: 10.1117/12.2266941; https://doi.org/10.1117/12.2266941
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