Cukurova University,Department of Textile Engineering, Main Branch of Textile Technology

Showing posts with label Image Processing. Show all posts
Showing posts with label Image Processing. Show all posts

October 08, 2023

Opportunities Offered by Image Processing Technology in Textile and Apparel

Humans have always used their senses to assess the quality of products at the personal level or in the industry. For example, when buying fruit, people examine the fruit for the parameters they consider are important for assessing its quality such as colour, uniformity of colour, surface roughness, size, shape, surface defect. However, they are not able to assess internal damages or constituents of the fruit because their vision is limited to visible spectrum which is part of a vast electromagnetic spectra. Also, in industrial situations, assessment of objects can be affected by tiredness and fatigue of inspectors as well as different inspectors may assess the required quality parameters differently. Machine vision that involves collecting information from objects in visible or other spectra and analyzing the obtained information using image processing steps can assess the quality consistently over a long period as well as assess the internal and external quality parameters. With advances in high quality sensors (cameras) for different spectra, computing power of computers, and ease of developing software for a specific application, it has been possible to apply machine vision to many fields covered in this book.

Chapter 7- Opportunities Offered by Image Processing Technology in Textile and Apparel
Abdurrahman Telli
Department of Textile Engineering, Cukurova University, Adana, Turkey

ABSTRACT: Resolution, speed, and quality of image acquisition systems have made great advances in recent years. In addition to this, the development of the software industry offers significant opportunities in many areas. The textile and apparel sector is one of these areas. Image processing studies provide new techniques in textile characterization. In textile quality control, it can replace the subjective evaluations that can lead to wrong evaluation results due to people’s inexperience, fatigue, and differences in perspectives. Image processing offers objective evaluation opportunities. In fabric defect detection, it helps to find defects either online or offline that the human eye cannot perceive. It prevents material and time wastage and increases quality. With the processing of the images taken from body scanning devices, more accurate information is obtained for clothing patterns. Digital libraries have been created by processing fiber, yarn, and fabric images. Design programs that include all stages from the yarn to the image of the garment on the model have been started to be used. Image processing offers opportunities to eliminate the damages caused by the fast fashion trend. In this chapter, current and potential usage possibilities of image processing technology in textile and apparel fields are discussed.

ISBN: 979-8-88697-975-6

Publication Date: September 15, 2023

Original Language: English

Document type: Book/ Restricted Access

Publisher: Nova Science



July 07, 2021

The Comparison of the Edge Detection Methods in the Determination of Yarn Hairiness through Image Processing

The resolution, quality and speed of the cameras have improved enormously in recent years. The combination of camera advancements and the software industry offers significant opportunities. 

In this study, an image processing approach for the determination of yarn hairiness was presented. Yarn images taken under a microscope were examined in MATLAB software. 

Seven different edge detection algorithms were used in order to separate the hairs from the yarn body. Seven different textural properties of obtained yarn images were compared with Zweigle hairiness test results. The findings have indicated that yarn hairiness can be clearly detected from microscope images with a six-step algorithm. 

The first four phases are grayscale, double format, 2D median filtering and histogram-fitting, respectively. The fifth stage is the edge detection algorithm and the sixth stage is the use of textural parameters. When compared with the Zweigle hairiness results, the most obvious finding to emerge from this study is that the best appropriate technique for edge detection was the Sobel method, and the textural parameter to be used in the evaluation was the standard deviation of matrix elements.

Source: Tekstil ve Konfeksiyon
Publisher: Ege University Textile and Apparel Research & Application Center
Original Language: English
Document type: Article / Open Access


May 30, 2019

Objective Measurement of Pilling Resistance in Knitted Fabrics with Image Processing Techniques

In the textile industry, measurement techniques based on image processing principles prepare to take over subjective evalution.

In this study, MATLAB software was used to evaluate pilling of knitted fabrics as objective. Knitted fabric images were taken in the cycles of 1000, 2000, 3000, 5000 and 7000.

Fabric’s surface digitization, pills detection and segmentation were carried out from these images.

Texture analysis was performed with Gray Level Co-occurrence Matrix (GLCM). After this phase, pill quantizations were made using images in matrix format obtained from image processing studies.

Standart deviation, entropy and mean of matrix elements, pill count, pill area and contrast values increased with the increase of rubbing cycles applied on the fabric.

Furthermore, the increase of rubbing cycles caused decrease in energy and homogeneity.

Source: Journal of Textiles and Engineer
Publisher: Chamber of Textile Engineering
Original Language: Turkish
Document type: Article / Open Access

Cited by 1 documents except author up to this time :

1. Kaynar Taşcı, Z., & Çelik, N. (2021). The Effect of Using Silver Fiber Content Yarns in Shirting Fabrics on Abrasion Resistance and Pilling Properties. Journal of Natural Fibers, 1-9.