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Automated visual inspection of patterned fabrics, rather than of plain and twill fabrics, has been increasingly focused on by our peers. The aim of this inspection is to detect, identify and locate any defects on a patterned fabric surface to maintain high quality control in manufacturing. This paper presents a novel Elo rating (ER) method to achieve defect detection in the spirit of sportsmanship, i.e., fair matches between partitions on an image. An image can be divided into partitions of standard size. With a start-up reference point, matches between various partitions are updated through an Elo point matrix. A partition with a light defect is regarded as a strong player who will always win, a defect-free partition is an average player with a tied result, and a partition with a dark defect is a weak player who will always lose.After finishing all matches, partitions with light defects accumulate high Elo points and partitions with dark defects accumulate low Elo points. Any partition with defects will be shown in the resultant thresholded image: a white resultant image corresponds to a light defect and a grey resultant image corresponds to a dark defect. The ER method was evaluated on databases of dot-patterned fabrics (110 defect-free and 120 defective images), star-patterned fabrics (30 defect-free and 26 defective images) and box-patterned fabrics (25 defect-free and 25 defective images). By comparing the resultant and ground-truth images, an overall detection success rate of 97.07% was achieved, which is comparable to the state-of-the-art methods.

This paper considers regularity analysis for patterned texture material inspection. Patterned texture-like fabric is built on a repetitive unit of a pattern. Regularity is one of the most important features in many textures. In this paper, a new patterned texture inspection approach called the regular bands (RB) method is described. First, the properties of textures and the meaning of regularity measurements are presented. Next, traditional regularity analysis for patterned textures is introduced. Many traditional approaches such as co-occurrence matrices, autocorrelation, traditional image subtraction and hash function are based on the concept of periodicity. These approaches have been applied for image retrieval, image synthesis, and defect detection of patterned textures. In this paper, a new measure of periodicity for patterned textures is described. The Regular Bands method is based on the idea of periodicity. A detailed description of the RB method with definitions, procedures, and explanations is given. There is also a detailed evaluation using the Regular Bands of some patterned textures. Three kinds of patterned fabric samples are used in the evaluation and a high detection success rate is achieved. Finally, there is a discussion of the method and some conclusions. Note to Practitioners-This paper is motivated by the study of a regularity feature for finding common properties in patterned textures. In general, regularity analysis of patterned textures involves two issues: the spatial relationship between intensity values and the repeat distance of a repetitive unit. These issues can also be defined as the periodicity of a patterned texture. However, the traditional periodicity is not effective for developing a patterned texture inspection algorithm. In this paper, a new measure for the regularity of patterned textures is designed for defect detection. It is based on the idea of applying the periodicity as a new principle for patterned texture inspection. A break in periodicity is considered to be a defect in patterned texture inspection. This concept has been applied to the development of a new method called the RB method. The regular band is defined by a moving average and standard deviation of the pixel intensity. It is specialized for defects which have differential intensity changes compared with the pattern on a repetitive unit of patterned texture. The RB method has been found very effective for defect detection of patterned fabric. In a comprehensive evaluation, the detection success rate of the RB approach has reached 99.4% in a total of 166 defective and defect-free images taken from three patterned fabrics. In this paper, the techniques and detection results of the RB method as well as comparisons with other methods are given. The computational time for processing an image of size 256 256 is only 140 ms using the C programming language. This new approach for automated patterned texture inspection is believed to be useful for quality control. It will also make contributions not only to practitio...

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