Illumination characteristics and image stitching for automatic inspection of bicycle part

Illumination characteristics and image stitching for automatic inspection of bicycle part Purpose – This study aims to investigate the spectral illumination characteristics and geometric features of bicycle parts and proposes an image stitching method for their automatic visual inspection. Design/methodology/approach – The unrealistic color casts of feature inspection is removed using white balance for global adjustment. The scale-invariant feature transforms (SIFT) is used to extract and detect the image features of image stitching. The Hough transform is used to detect the parameters of a circle for roundness of bicycle parts. Findings – Results showed that maximum errors of 0°, 10°, 20°, 30°, 40° and 50° for the spectral illumination of white light light-emitting diode arrays with differential shift displacements are 4.4, 4.2, 7.8, 6.8, 8.1 and 3.5 per cent, respectively. The deviation error of image stitching for the stem accessory in x and y coordinates are 2 pixels. The SIFT and RANSAC enable to transform the stem image into local feature coordinates that are invariant to the illumination change. Originality/value – This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

Illumination characteristics and image stitching for automatic inspection of bicycle part

Assembly Automation, Volume 34 (4): 7 – Sep 9, 2014

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0144-5154
DOI
10.1108/AA-09-2013-076
Publisher site
See Article on Publisher Site

Abstract

Purpose – This study aims to investigate the spectral illumination characteristics and geometric features of bicycle parts and proposes an image stitching method for their automatic visual inspection. Design/methodology/approach – The unrealistic color casts of feature inspection is removed using white balance for global adjustment. The scale-invariant feature transforms (SIFT) is used to extract and detect the image features of image stitching. The Hough transform is used to detect the parameters of a circle for roundness of bicycle parts. Findings – Results showed that maximum errors of 0°, 10°, 20°, 30°, 40° and 50° for the spectral illumination of white light light-emitting diode arrays with differential shift displacements are 4.4, 4.2, 7.8, 6.8, 8.1 and 3.5 per cent, respectively. The deviation error of image stitching for the stem accessory in x and y coordinates are 2 pixels. The SIFT and RANSAC enable to transform the stem image into local feature coordinates that are invariant to the illumination change. Originality/value – This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching.

Journal

Assembly AutomationEmerald Publishing

Published: Sep 9, 2014

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