Abstract
Abstract Image processing of digital images is one of the essential categories of image transformation in the theory and practice of digital pattern analysis and computer vision. Automated pattern recognition systems are much needed in the textile industry more importantly when the quality control of products is a significant problem. The printed fabric pattern segmentation procedure is carried out since human interaction proves to be unsatisfactory and costly. Hence, to reduce the cost and wastage of time, automatic segmentation and pattern recognition are required. Several robust and efficient segmentation algorithms are established for pattern recognition. In this paper, different automated methods are presented to segregate printed patterns from textiles fabric. This has become necessary because quality product devoid of any disturbances is the ultimate aim of the textile printing industry.
Highlights
Printed fabric pattern segmentation is the partitioning of fabric pattern into a set of disjoint regions with uniform and similar characteristics [1, 2]
Even though there is no general solution to the pattern image segmentation problem, techniques frequently have to be combined with domain knowledge to solve an image segmentation problem successfully [11]
Several improved segmentation methodologies for printed fabric patterns have been projected over the years which demonstrated inherently appropriate in textiles quality control evaluation
Summary
Printed fabric pattern segmentation is the partitioning of fabric pattern into a set of disjoint regions with uniform and similar characteristics [1, 2]. Chung-Feng et al established that the FCM fuzzy clustering algorithm and the SC cluster-validity index are very real for evaluating color features and pattern features of printed fabric automatically [18, 23, 24] This method examines colors and patterns on printed material without a priori information about data structures, and the clustering algorithm segregates the dataset into clusters such that data within a group are more similar objects to each other than to those belonging to different clusters. To substantially analyze automatic color and pattern separation of printed fabrics, [24, 25] adopted region splitting method where all color variance points in the pattern image are subjected to subregion to reduce the interfering [26] This method is enhanced with the FCM algorithm with a specific clustering number to automatically segment the image into two subdivisions and decide whether further separation is necessary by assessing automatic color uniformity [27]. The segmentation process is iterative if needed with the specific clustering number until the consistency of each subregion is achieved and can no longer be segmented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
