Abstract
A new approach for extracting the hairiness from fabric based on the predicted fabric surface plane is presented in this paper to extract the hairiness from the depth image. The depth from focus (DFF) technique is utilized in this study to establish the depth image of the pilled fabrics by using a series of image layers captured under a microscope. A pilled fabric depth image provides information on the hairiness and the fabric surface, and the hairiness is located above the fabric surface. However, the depth value of the fabric surface covered with hairiness cannot be directly obtained. Therefore, for hairiness extraction, a predicted plane of the fabric surface is fitted by selecting several base points on the fabric surface. The target above the predicted plane will be considered as hairiness and will be extracted. The oversegmentation method based on the mean shift algorithm is used in the study to select the base points of the fabric surface. First, several seed points are marked along the Sobel edges; then, several oversegmented areas are formed after the growth of the seed points, which are called split pieces in this paper. The split pieces of the fabric surfaces are selected as the base points according to the depth value as well as the spatial direction of each split piece. Finally, the predicted plane of the fabric surface is established using these base points. The results of significance testing show that is it reasonable to assume that the fabric surface can be expressed as a plane. The results of the residual examination show that the predicted plane can correctly calculate the depth value (z) of the fabric surface at any plane position (x, y). The extracted hairiness images show that hairiness can be correctly and completely obtained through the predicted plane.
Highlights
Pilling ruins the original appearance of a fabric
The depth value (z) of each point on the fabric surface is supposed to have a linear relationship with the spatial coordinates (x, y)
This paper provided a method of extracting hairiness from the fabric background based on the predicted fabric surface plane
Summary
The pilling grade of a fabric is assessed through manual observation by comparing the sample with standard pilling photographs. This subjective method can be incorrect and inconsistent since the evaluator’s experience, psychology and physiology may affect the correctness of the assessment result [1]. Furferi et al [3] devised a machine vision-based procedure with the aim of extracting a number of parameters that characterize the fabric. These parameters were used to train an artificial neural network to automatically grade the fabrics in terms of pilling. Guan et al [5] developed an
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