A computer vision system for objective fabric smoothness appearance assessment with an ensemble classifier. Issue 3 (February 2020)

• Record Type: Journal Article
• Authors: Wang, Jingan; Shi, Kangjun; Wang, Lei; Pan, Ruru; Gao, Weidong
• Is Part Of: Textile research journal. Volume 90:Issue 3/4(2020)
• Page Start: 333
• Page End: 343
• Publication Date: 2020-02
• Subjects: Fabric smoothness -- textile testing -- ensemble classifier -- computer vision system; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517519866951
• Languages: English
• ISSNs: 0040-5175

Abstract

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. To evaluate fabric smoothness objectively, different methods have been proposed based on computer vision technology. To further improve the performance and promote the application of the assessment methods, this paper reports a hybrid computer vision system for objective assessment of fabric smoothness appearance with an ensemble classifier to integrate the advantages of the different image feature sets, which are extracted based on different image processing technologies. The image acquisition environment is established in this system with the selection of illumination parameters—intensity, position angle and altitudinal angle—by a designed strategy. The main steps of the strategy include determination of priority by information gain analysis and parameter selection by classifier performance analysis. The support vector machine classifiers trained by each feature sets are grouped into an ensemble by a self-adapting weighted voting method and the redundant feature sets are eliminated based on the weights of the feature sets. The final result shows evaluation accuracies with 82.86% under 0-degree error, 97.14% under 0.5-degree error and 100% under 1-degree error, which outperforms the other methods in the same environment and verifies the applicability of the proposed system.