The purpose of this paper is to provide a robust method for automatic detection of seam lines based only on digital images of the garments.
A local standard deviation pre‐processing filter is applied to enhance the contrast between the seam line and the texture and the Prewitt operator extracts the edges of the enhanced image. The seam line is detected by a maximum at the Radon transform. The proposed method is invariant to the illumination intensity and it has been also tested with moving average and fast Fourier transform low‐pass filters used in the pre‐processing module. Extensive experiments are carried out in the presence of additive Gaussian and uniform noise.
The proposed method detects 109 out of 118 seams when the local standard deviation is used at the pre‐processing stage, giving a mean distance error between the real and the estimated line of 2 mm when the image is digitised at 97 dpi. However, in case the images are distorted by additive Gaussian noise at 20 dB signal‐to‐noise ratio, the moving average low‐pass filtering method gives the best results, detecting 104 noisy images.
The proposed method detects seam lines that can be approximated by a continuation of straight lines. The current work can be extended in the detection of the curved parts of seam lines.
Since the method addresses garments instead of seam specimens, the proposed approach can be imported in automatic systems for online quality control of seams.
Local standard deviation belongs to first‐order statistics, which makes it suitable for texture analysis and that is why it is mostly used in web defect detection. The novelty in the approach, however, is that by considering the seam as an abnormality of the texture, the authors applied that method at the pre‐processing stage to enhance the seam before the detection. Moreover, the presented method is illumination invariant, a property that has not been addressed in similar methods.
Mariolis, I. and Dermatas, E. (2009), "Illumination invariant seam line detection in real garments", International Journal of Clothing Science and Technology, Vol. 21 No. 5, pp. 286-299. https://doi.org/10.1108/09556220910983786Download as .RIS
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