Weave-repeat identification by structural analysis of fabric images

Ralló, Miquel; Escofet, Jaume; Millán, Marı́a S.

doi:10.1364/ao.42.003361

Cited by 35 publications

(20 citation statements)

References 6 publications

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“…Based on these principles and the use of the fast Fourier transform (FFT), computationally efficient methodologies for analysing woven textile images have been developed and applied to automated defect detection and weave pattern identification in textiles [23,24]. The applicability of these techniques in the (ii) Application of fast Fourier transform to calculate ( ) …”

Section: Image Analysis Methodologymentioning

confidence: 99%

Stochastic simulation of woven composites forming

Skordos

Sutcliffe

2008

Composites Science and Technology

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Section: Image Analysis Methodologymentioning

confidence: 99%

Stochastic simulation of woven composites forming

Skordos

Sutcliffe

2008

Composites Science and Technology

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“…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%

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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“…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]. The outcome of this stage is a weave pattern diagram showing the warp over weft or weft over warp in each cell of cross points.…”

Section: Crossed-states Detectionmentioning

confidence: 99%

A generic approach to haptic modeling of textile artifacts

Shidanshidi

Naghdy

et al. 2009

Mathematics for Signal and Information Processing

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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 measurement of textile properties in construction of the mechanical model. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure for the textile, and deploys computational intelligence to estimate the mechanical properties of textile. 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 geometrical model. Digital image processing is applied to the still image of textile to identify its pattern and structure through a fuzzy rule-base algorithm. The 3D geometric model of the artifact is automatically generated in VRML based on the identified pattern and structure obtained from the textile image. Selected mechanical properties of the textile are estimated by an artificial neural network; deploying the textile geometric characteristics and yarn properties as inputs. The estimated mechanical properties are then deployed in the construction of the textile mechanical model. The proposed system is introduced and the developed algorithms are described. The validation of method indicates the feasibility of the approach and its superiority to other haptic modeling algorithms. ABSTRACTHaptic 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 measurement of textile properties in construction of the mechanical model. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure for the textile, and deploys computational intelligence to estimate the mechanical properties of textile. 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 geometrical model. Digital image processing is applied to the still image of textile to identify its pattern and structure through a fuzzy rule-base algorithm. The 3D geometric model of the artifact is automatically generated in VRML based on the identified pattern and structure obtained from the textile image. Selected mechanical properties of the textile are estimated by an artificial neural network; deploying the textile geometric characteristics and yarn properties as inputs. The estimated mechanical properties are then deployed in the construction of the textile mechanical model. The proposed system is introduced and the developed algorithms are described. The validation of method indicates the feasibility of the approach and its ...

show abstract

Weave-repeat identification by structural analysis of fabric images

Cited by 35 publications

References 6 publications

Stochastic simulation of woven composites forming

Stochastic simulation of woven composites forming

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

A generic approach to haptic modeling of textile artifacts

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