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Cloth Type Detection Using Segmentation And Classifier

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Abstract:

Quality inspection is an important aspect of modern industrial manufacturing. In textile industry production, automate fabric inspection is important for maintain the fabric quality. For a long time the fabric defects inspection process is still carried out with human visual inspection, and thus, insufficient and costly. Therefore, automatic fabric defect inspection is required to reduce the cost and time waste caused by defects. The development of fully automated web inspection system requires segmentation and classification of detection algorithms. The detection of local fabric defects is one of the most intriguing problems in computer vision. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. Various approaches for fabric defect detection have been proposed in past and the purpose of this paper is to categorize and describe these algorithms. This paper attempts to present the survey on fabric defect detection techniques, with a comprehensive list of references to some recent.

 

Introduction:

Quality assurance of product is considered as one of the most important focuses in the industrial production. So is textile industry too. Textile product quality is seriously degraded by defects. So, early and accurate fabric defect detection is an important phase of quality control. Manual inspection is time consuming and the level of accuracy is not satisfactory enough to meet the present demand of the highly competitive international market. Hence, expected quality cannot be maintained with manual inspection. Automated, i.e. computer vision based fabric defect inspection system is the solution to the problems caused by manual inspection. Automated fabric defect inspection system has been attracting extensive attention of the researchers of many countries for years. The high cost, along with other disadvantages of human visual inspection has led to the development of automated defect inspection systems that are capable of performing inspection tasks automatically. The global economic pressures have gradually led business to ask more of itself in order to become more competitive. As a result, intelligent visual inspection systems to ensure high quality of products in production lines are in increasing demand of printed textures (e.g. printed fabrics, printed currency, wall paper) requires evaluation of color uniformity and consistency of printed patterns, in addition to any discrepancy in the background texture, but has attracted little attention of researchers. Human inspection is the traditional means to assure the quality of fabric. It helps instant correction of small defects, but human error occurs due to fatigue and fine defects are often undetected. Therefore, automated inspection of fabric defect becomes a natural way to improve fabric quality and reduce labor costs.

 

 Existing System:

An automated defect detection and identification system enhances the product quality and results in improved productivity to meet both customer needs and to reduce the costs associated with off-quality. The inspection of real textile defects is particularly challenging due to the large number of textile defect classes, which are characterized by their vagueness and ambiguity classifies.

 

Proposed System:

Here we find with the HSV conversion to find the texture and shape of cloth  by processing we improve the quality by segmentation and reducing the noise  and makes simple to find the color features in it and it classifies by the feature matching classification.

 

Block  Diagram:

Cloth Type Detection Using Segmentation

 

 

 

Advantages:

  • Different type of datasets can accept
  • Classification accuracy  is clear

 

Applications:

  • Cloth industrial applications
  • Shopping malls applications

 

 Software Requirement:

  • MATLAB 7.14 and above versions

 

Conclusion:

In this paper, we proposed recognition types of clothing by using a combination HSV conversion and feature matching classification base on Bag of Features. The three sub-windows can optimize and improve the performance of interest point detection with height accuracy score. The experiment showed the proposed method achieves precision score 73.57%.

 

 References:

[1] M. Mizuochi, A. Kanezaki, and T. Harada, “Clothing Retrieval Based on Local Similarity with Multiple Images,” presented at the Proceedings of the ACM International Conference on Multimedia, Orlando, Florida, USA, 2014.

[2] S. O’Hara and B. A. Draper, “Introduction to the Bag of Features Paradigm for Image Classification and Retrieval,” Computing Research Repository (CoRR), 2011.

[3] A. Nodari, M. Ghiringhelli, A. Zamberletti, M. Vanetti, S. Albertini, and I. Gallo, “A mobile visual search application for content based image retrieval in the fashion domain,” in Content-Based Multimedia Indexing (CBMI), 2012 10th International Workshop on, 2012, pp. 1-6.

[4] G. A. Cushen and M. S. Nixon, “Mobile visual clothing search,” in Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on, 2013, pp. 1-6.

[5] S. Miura, T. Yamasaki, and K. Aizawa, “SNAPPER: Fashion Coordinate Image Retrieval System,” in Signal-Image Technology & Internet-Based Systems (SITIS), 2013 International Conference on, 2013, pp. 784-789.

[6] X. Yuan;, J. Yu;, Z. Qin;, and T. Wan, “A SIFT-LBP IMAGE RETRIEVAL MODEL BASED ON BAG-OF-FEATURES,” Proceedings of the International Conference on Image Processing (ICIP 2011), pp. 1061-1064, 2011.

[7] S. Banerji, A. Sinha, and C. Liu, “A New Bag of Words LBP (BoWL) Descriptor for Scene Image Classification,” in Computer Analysis of Images and Patterns. vol. 8047, R. Wilson, E. Hancock, A. Bors, and W. Smith, Eds., ed: Springer Berlin Heidelberg, 2013, pp. 490-497.

[8] P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” Int. J. Comput. Vision, vol. 57, pp. 137-154, 2004. [9] D. A. Jusko. (2015, July, 7). Human Figure Drawing Proportions.

[10] C. Rother, V. Kolmogorov, and A. Blake, “”GrabCut”: interactive foreground extraction using iterated graph cuts,” presented at the ACM SIGGRAPH 2004 Papers, Los Angeles, California, 2004.

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