&lt;title&gt;Image segmentation based on a Gaussian model applied to pilling evaluation in fabrics&lt;/title&gt;

Abril, Hector C.; Millán, Marı́a S.; Moreno, Yezid M. Torres; Navarro, Rafael

doi:10.1117/12.281290

Cited by 5 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10 and several improvements were introduced later. 11 The analysis of the set of standard images enables us to approach empirically the underlying relationship between the total area of pilling and the degree of pilling attributed to the standard samples. In addition, our automatic method is compared in this work to human observers ͑assisted by computer͒.…”

Section: Introductionmentioning

confidence: 99%

Automatic method based on image analysis for pilling evaluation in fabrics

et al. 1998

View full text Add to dashboard Cite

A fully automatic method for pilling evaluation in wear-andtear fabrics is developed from the image analysis of a set of standard photographs (Zweigle KG-741 Reutlingen). The method involves operations in both the spatial and frequency domains to segment pills from the textured background of the web. It calculates the total area of pilling in the sample image and assigns a degree of pilling according to the standard. Two mathematical descriptions are analyzed according to the underlying rule established by the standard images, using the visual estimation of the area of pilling performed by a group of observers. A logarithmic (in base two) approach, which is consistent with human visual perception laws and facilitates an optimization of the method, is eventually adopted.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic method based on image analysis for pilling evaluation in fabrics

et al. 1998

View full text Add to dashboard Cite

show abstract

“…In [9] pill regions on fabric samples are localized by combining template matching techniques and image thresholding. In [10] operations in both the spatial and frequency domains are introduced to segment pills from the textured background of the fabric web. Such a method calculates the total area occupied by pills in the sample image and assigns a degree of pilling.…”

Section: Introductionmentioning

confidence: 99%

Towards Automated and Objective Assessment of Fabric Pilling

Furferi

Carfagni

Governi

et al. 2014

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Pilling is a complex property of textile fabrics, representing, for the final user, a non-desired feature to be controlled and measured by companies working in the textile industry. Traditionally, pilling is assessed by visually comparing fabrics with reference to a set of standard images, thus often resulting in inconsistent quality control. A number of methods using machine vision have been proposed all over the world, with almost all sharing the idea that pilling can be assessed by determining the number of pills or the area occupied by the pills on the fabric surface. In the present work a different approach is proposed: instead of determining the number of pills, a machine vision-based procedure is devised with the aim of extracting a number of parameters characterizing the fabric. These are then used to train an artificial neural network to automatically grade the fabrics in terms of pilling. Tested against a set of differently pilled fabrics, the method shows its effectiveness.

show abstract

“…In particular, the authors claim that a logarithmic relationship between the total pilling area and the degree of pilling subsists observed (see Figure 7). The proposed method has been further implemented by the same authors in [9] by using 1) a Top-hat transform (an operation that extracts small elements and details from given images [10]) for obtaining background uniformity, 2) an image segmentation based on a Gaussian model [11] of the background, and 3) a selective noise elimination in the binary image. The maximum error of misclassification in percent of background pixels in the total amount of pixels classified as pilling (beyond the threshold) was found equal to 0.3% for an optimally selected threshold value.…”

mentioning

confidence: 99%

Machine Vision-Based Pilling Assessment: A Review

Furferi

Governi

Volpe

2015

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

Pilling is an undesired defect of textile fabrics, consisting of a surface characterized by a number of roughly spherical masses made of entangled fibers. Mainly caused by the abrasion of fabric surface occurring during washing and wearing of fabrics, this defect needs to be accurately controlled and measured by companies working in the textile industry. Pilling measurement is traditionally performed using manual procedures involving visual control of fabric surface by human experts. Since the early nineties, great efforts in developing automatic and non-intrusive methods for pilling measurement have been made all around the world with the final aim of overcoming traditional, visual-based and subjective procedures. Machine Vision proved to be among the best options to perform such defect assessment since it provided increasingly performing measurement equipment and tools, serving the purpose of automatic control. In particular, a relevant number of interesting works have been proposed so far, sharing the idea of helping (or even replacing) traditional measurement methods using image processing-based ones. The present work provides a rational and chronological review of the most relevant methods for pilling measurement proposed so far. This work serves the purposes of 1) understanding whether today's automatic machine vision-based pilling measurement techniques are ready for supplanting traditional pilling measurement and 2) providing textile technology researchers with a bird's eye view of the main methods studied to confront with this problem.

show abstract

<title>Image segmentation based on a Gaussian model applied to pilling evaluation in fabrics</title>

Cited by 5 publications

References 5 publications

Automatic method based on image analysis for pilling evaluation in fabrics

Automatic method based on image analysis for pilling evaluation in fabrics

Towards Automated and Objective Assessment of Fabric Pilling

Machine Vision-Based Pilling Assessment: A Review

Contact Info

Product

Resources

About