Textile Woven-fabric Recognition by Using Fourier Image-analysis Techniques: Part I: A Fully Automatic Approach for Crossed-points Detection

Lachkar, A.; Gadi, Taoufiq; Benslimane, Rachid; D’Orazio, L.; Martuscelli, E.

doi:10.1080/00405000308630608

Cited by 29 publications

(22 citation statements)

References 2 publications

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“…Wang et al (2010) have mentioned a number of well developed computer vision and image processing algorithm provide solid bases for automatic recognition of woven fabric patterns and automatic measurement of structural characteristics such as warp and fill counts in weaving technology. Kinnoshita et al (1989), Wood (1990), Ravandi and Toriumi (1995), Xu (1996), Campbell and Murtagh (1998), Kang et al (1999), Huang et al (2000), Jeon et al (2003), Rallo et al (2003), Lachkar et a. (2003), Kuo et al (2004), Lachkar et al (2005), Kuo et al (2005), and Kuo and Tsai (2006) have reported on the automatic analysis of woven textile structures.…”

Section: Fig 1: Basic Components Of a Tensioned Fabric Structurementioning

confidence: 99%

Proposed Algorithm for Warp Direction Checking in Tensioned Fabric Structures

Yee¹,

Choong²

2013

IJSRK

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Abstract. Mesh generation of tensioned fabric structure is needed before form-finding. The sequence of defining the mesh of a surface must ensure that the warp and fill direction of the fabric material used in the model is properly represented due to orthotropic nature of fabric material. Therefore, the checking of orientation of mesh is essential because the orientation of element nodes directly affects the direction of material axes. The manual checking of orientation of mesh is not sufficient to give the accurate and precise result and it is time-consuming. There is no such research to index and search for orientation of mesh specially. Procedure for changing node numbering sequence of elements in a mesh of FE model for initial assumed shape has been proposed in order to obtain a correct warp direction in elements. Results show that the proposed algorithm for warp direction checking can achieve substantially accurate classification.

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Section: Fig 1: Basic Components Of a Tensioned Fabric Structurementioning

confidence: 99%

Proposed Algorithm for Warp Direction Checking in Tensioned Fabric Structures

Yee¹,

Choong²

2013

IJSRK

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“…• Employing Fourier filtering techniques to find periodic weave pattern in a woven fabric image by either identification of the peaks in the power spectrum image [5][6][7] or finding the peak points of the autocorrelation function of the gray level data in warp and weft directions [8].…”

Section: Crossed-points Detectionmentioning

confidence: 99%

3D Geometric and Haptic Modeling of Hand-Woven Textile Artifacts

Shidanshidi

Naghdy

et al. 2010

IFIP Advances in Information and Communication Technology

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Abstract. Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require time-consuming objective measurement of textile properties in mechanical/physical model construction. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure textile artifact, and deploys computational intelligence to estimate textile mechanical and physical properties. The approach is designed primarily for display of textile artifacts in museums. The haptic model is constructed by superimposing the mechanical model of textile over its 3D geometrical model. Digital image processing is applied to the still image of textile to identify its pattern and structure. In order to deal with the nonlinearities associated with the textile, a fuzzy rule-based expert system is deployed. This information is then used to generate a 3D geometric model of the artifact in VRML. Selected mechanical and physical properties of the textile are estimated by an artificial neural network with the textile geometric characteristics and yarn properties as inputs. The neural network learning and verification and validation processes are carried out by a sample data set. The mechanical properties are used in the construction of the textile mechanical model. The haptic rendered model is generated by superimposing the physical/mechanical model over the 3D geometric model. This model has been implemented and rendered in Reachin environment, provided an interactive Virtual Reality environment where the user can navigate the graphic 3D presentation of the textile and touch it by a haptic device. Different samples have been modeled and the whole approach has been validated. The interface can be provided in both in the physical environment and through the cyberspace. The validation of method indicates the feasibility of the approach and its superiority to other haptic modeling algorithms.

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“…Scanning and pre-processing the textile samples (b) Detection of crossed-points that is concerned with detecting the interlacing areas between wrap and weft yarns, and (c) Detection of crossed-states that determines which yarn is over the other in the interlacing areas [9,10].…”

Section: Automatic Recognition Of Woven Fabric Structurementioning

confidence: 99%

“…They are based on two primary methods. The Focus of the first method is on Fourier filtering techniques for finding periodic weave pattern in a woven fabric image by either identification of the peaks in the power spectrum image [5,6,9] or finding the peak of autocorrelation function of the gray level data in warp and weft directions [3]. In the second method, the crossed-points are found by finding the peaks in accumulation gray level values in vertical and horizontal directions pixels [4,11].…”

Section: Crossed-points Detectionmentioning

confidence: 99%

3D geometric modelling of hand-woven textile

Shidanshidi

Naghdy

et al. 2008

Image Processing: Machine Vision Applications

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Geometric modeling and haptic rendering of textile has attracted significant interest over the last decade. A haptic representation is created by adding the physical properties of an object to its geometric configuration. While research has been conducted into geometric modeling of fabric, current systems require timeconsuming manual recognition of textile specifications and data entry. The development of a generic approach for construction of the 3D geometric model of a woven textile is pursued in this work. The geometric model would be superimposed by a haptic model in the future work. The focus at this stage is on hand-woven textile artifacts for display in museums. A fuzzy rule based algorithm is applied to the still images of the artifacts to generate the 3D model. The derived model is exported as a 3D VRML model of the textile for visual representation and haptic rendering. An overview of the approach is provided and the developed algorithm is described. The approach is validated by applying the algorithm to different textile samples and comparing the produced models with the actual structure and pattern of the samples. ABSTRACTGeometric modeling and haptic rendering of textile has attracted significant interest over the last decade. A haptic representation is created by adding the physical properties of an object to its geometric configuration. While research has been conducted into geometric modeling of fabric, current systems require time-consuming manual recognition of textile specifications and data entry. The development of a generic approach for construction of the 3D geometric model of a woven textile is pursued in this work. The geometric model would be superimposed by a haptic model in the future work. The focus at this stage is on hand-woven textile artifacts for display in museums. A fuzzy rule based algorithm is applied to the still images of the artifacts to generate the 3D model. The derived model is exported as a 3D VRML model of the textile for visual representation and haptic rendering. An overview of the approach is provided and the developed algorithm is described. The approach is validated by applying the algorithm to different textile samples and comparing the produced models with the actual structure and pattern of the samples.

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Textile Woven-fabric Recognition by Using Fourier Image-analysis Techniques: Part I: A Fully Automatic Approach for Crossed-points Detection

Cited by 29 publications

References 2 publications

Proposed Algorithm for Warp Direction Checking in Tensioned Fabric Structures

Proposed Algorithm for Warp Direction Checking in Tensioned Fabric Structures

3D Geometric and Haptic Modeling of Hand-Woven Textile Artifacts

3D geometric modelling of hand-woven textile

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