Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method

Kuo, Sy‐Yen; Shih, Chung‐Yang; Lee, Jiunn‐Yih

doi:10.1177/004051750407400204

Cited by 96 publications

(74 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Wu et al (2005) have developed a semi-automatic identification system of weave patterns for double-layer weft woven necktie fabric.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…• Identifying the peaks in accumulation gray level values in vertical and horizontal directions pixels [9,10].…”

Section: Crossed-points Detectionmentioning

confidence: 99%

“…A variety of methods have been suggested in the literature for crossed-states detection including employing texture orientation features in each one of the detected cells [11], normalized aspect ratio of an ellipse-shaped image at crossed points of the fabric [12], fuzzy c-means clustering [9,10], and Fourier image analysis techniques [5,7,[13][14][15] In this study, the yarn edges-texture orientation features of each side of the cell are calculated for each detected cell. The derived features are then transformed to frequency domain by deploying Fast Fourier Transform.…”

Section: Crossed-states Detectionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shidanshidi

Naghdy

et al. 2010

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

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.

show abstract

“…Various methods such as neural networks [3] ,fuzzy logic [4] and Fourier image analysis techniques [5][6][7] have been applied to still images of the textile artifact with varying degrees of success. The major drawback of such methods has been the rigidity of the models identified for the artefacts.…”

Section: Introductionmentioning

confidence: 99%

3D geometric modelling of hand-woven textile

Shidanshidi

Naghdy

et al. 2008

Image Processing: Machine Vision Applications

View full text Add to dashboard Cite

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.

show abstract

Automatic Recognition of Fabric Weave Patterns by a Fuzzy C-Means Clustering Method

Cited by 96 publications

References 9 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

Contact Info

Product

Resources

About