Artificial vision based inspection of marbled fabric

Authors:
Rocco Furferi;Lapo Governi;Matteo Palai;Yary Volpe
Affiliations:
Department of Mechanics and Industrial Technologies, Università degli Studi di Firenze, Firenze, Italy;Department of Mechanics and Industrial Technologies, Università degli Studi di Firenze, Firenze, Italy;Department of Mechanics and Industrial Technologies, Università degli Studi di Firenze, Firenze, Italy;Department of Mechanics and Industrial Technologies, Università degli Studi di Firenze, Firenze, Italy
Venue:
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
Year:
2011

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Abstract

Marbling effect on fabrics is a relevant aesthetic feature, increasing its diffusion specially in the field of textiles for technical applications. The fabric aesthetic anisotropy, characterizing the marbling effect, has a strong impact on the perceived quality: a high-quality marbled fabric to be used in automotive textiles, for instance, is characterized by a tiny quantity of veins and spotted areas. A large amount of "veins" and/or discolored areas may induce a customer to consider the fabric as "defected". In common practice, the identification of whether the fabric is defective or not is performed by human experts by means of visual inspection. As a consequence, fabric inspection is performed in a qualitative and unreliable way; thereby the definition of a method for the automatic and objective inspection is advisable. On the basis of the state of the art, the present work aims to describe a computer-based approach for the automated inspection of marbling effect on fabrics, resulting in the classification of fabrics into three quality classes. The devised apparatus is composed by a machine vision system provided with an image processing-based software. The processing software is able to determine the anisotropy of a fabric using edge segmentation and image entropy and defining a "fabric entropy curve". The proposed method proves to be able to classify the fabrics into the correct quality class in 90% of the cases, with respect to the selection criteria provided by human operators.