Carpet wear classification based on support vector machine pattern recognition approach

Copot, Cosmin; Syafiie, S.; Vargas, Sergio Alejandro Orjuela; Keyser, Robain De; Langenhove, Lieva Van; Lazăr, Corneliu

doi:10.1109/iccp.2009.5284769

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…To extract texture features from these data using image analysis, the information of depth had to be structured first into 2D images. This has two disadvantages, an additional computational cost and the possibility of distortion of the surface shape due to interpolation methods involved in this process [6]. Although a classification over 95% was achieved, the extracted features did not change accordingly with the wear labels as should be expected.…”

Section: Introductionmentioning

confidence: 93%

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Vargas

Jaramillo²,

Meulemeester

et al. 2010

Advanced Concepts for Intelligent Vision Systems

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Abstract. Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant.

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Section: Introductionmentioning

confidence: 93%

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Vargas

Jaramillo²,

Meulemeester

et al. 2010

Advanced Concepts for Intelligent Vision Systems

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“…Texture synthesis of various materials including textiles by SVM was examined by Dong et al (2008). Copot et al (2009) studied the application of SVM for carpet wear classification. More recently, least-square support vector machine (LS-SVM) has proved to be a more reliable and efficient classifier among other variants of SVMs (Suykens and Vandewalle, 1999).…”

Section: Introductionmentioning

confidence: 99%

Categorization of fabric design using multi-class least-square support vector machine

Ghosh¹,

Guha²,

Bhar

2014

International Journal of Clothing Science and Technology

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Purpose – The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM). Design/methodology/approach – In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features. Findings – The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM. Originality/value – The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.

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“…Recently, we presented a scanner specifically designed for scanning carpets using structured light triangulation [15]. These type of scanners have the advantage of low color sensitivity compared to other 3D imaging methods [16][17][18]. The depth information is digitized into a range image, where the pixels of the image represent depth.…”

Section: Introductionmentioning

confidence: 99%

Automatic grading of appearance retention of carpets using intensity and range images

Vargas

Ortiz-Jaramillo

Vansteenkiste

et al. 2012

J. Electron. Imaging

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Abstract. Textiles are mainly used for decoration and protection. In both cases, original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, while and after manufacturing, to determine lifetime and/or beauty of textile products. Particularly, appearance retention of textile products is commonly certified with grades, which are currently assigned by human experts. However, manufacturers would prefer a more objective system. In this paper we present an objective system for grading appearance retention, particularly, for textile floor coverings.Changes in appearance are quantified by using linear regression models on texture features extracted from intensity and range images. Range images are obtained by our own laser scanner, reconstructing the carpet surface using two methods that have been previously presented. We extract texture features using a variant of the local binary pattern technique based on detecting those patterns whose frequencies are related to the appearance retention grades. We test models for eight types of carpets. Results show that the proposed approach describes the degree of wear with a precision within the range allowed to human inspectors by international standards. The methodology followed in this experiment has been designed to be general for evaluating global deviation of texture in other types of textiles as well as other surface materials.

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Carpet wear classification based on support vector machine pattern recognition approach

Cited by 7 publications

References 10 publications

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Categorization of fabric design using multi-class least-square support vector machine

Automatic grading of appearance retention of carpets using intensity and range images

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