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.
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.
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.
This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching.
Chang, W.-Y. and Tsai, C.-P. (2014), "Illumination characteristics and image stitching for automatic inspection of bicycle part", Assembly Automation, Vol. 34 No. 4, pp. 342-348. https://doi.org/10.1108/AA-09-2013-076
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