Textile Pattern Design Based on IFS

Meng, Guang Li; Xu, Yue; Li, Dong Xia

doi:10.4028/www.scientific.net/amm.251.239

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Some study has suggested to recognize and reconstruct weave patterns with computer-aided image analysis techniques, 4 but not purposed to invent new ones. Researchers have reported textile pattern generation by visualizing the famous fractal models, including iterated function systems, 5,6 the Julia set, 7,8 and the L systems. 9,10 Another important model for visualization is the weak chaos system 11 upon which there are the derived mechanisms like Quasi-regular system, 12 uniform stochastic web, 13 and Hamiltonian function.…”

Section: Introductionmentioning

confidence: 99%

Modeling and refactoring the indigo patterns

Chen

et al. 2019

Journal of Engineered Fibers and Fabrics

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For purpose of reconstruction and innovation of indigo patterns, this study explores modeling, reconstruction, and assembling of the pattern elements by means of mathematics. A model for indigo pattern elements is proposed based on cardinal splines, in which the rigidity of shape is conveyed by the tension coefficient, and the concavity and variety by configuration of the knots. The generalized version of this model is capable of covering any complex element. The contour tracing technique is employed to extract pattern elements from the image, and the closest model instance is selected in virtue of invariance of the improved Hu moments. The selected instances are transformed with respect to the geometric center, the coverage, and the coincidence to match the pattern elements in the image so as to reconstruct the whole pattern. The element filters are conducted on the reconstructed patterns to modify the elements and produce new innovative patterns of constant skeleton. The model is borrowed serving as a skeleton in element assembling. The skeleton properties are investigated to provide basis for skeleton embodiment in which these properties are involved into establishing the placement determiner and element determiner so as to carry out the assembling of elements. Also discussed is the extended skeleton which goes beyond the model and brings about variety and flexibility to element assembling. It is turned out that reconstruction with the model well implements a mathematical copy of the pattern, and the assembling of elements by skeletons provides rich possibilities to innovation of indigo patterns.

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Section: Introductionmentioning

confidence: 99%

Modeling and refactoring the indigo patterns

Chen

et al. 2019

Journal of Engineered Fibers and Fabrics

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“…There are many endeavors carried out on pattern synthesis, or broadly speaking, image synthesis, e.g., using Markov random field model to approximate an image to different levels by tuning the neighborhood window (Efros and Leung, 2002), using convolutional neural network (CNN) to learn image features across the layers with texture representations that are increasingly explicit (Gatys et al , 2015), integrating depth data captured by a Microsoft Kinect device into the image input to a CNN to achieve 3D textile reconstruction (Hu et al , 2017). While the digitalized pattern creation is primarily inspired by the visualization of mathematical models, among which there are the famous fractal algorithms including iterated function systems (Guo and Song, 2010; Meng et al , 2013), the Julia set (Ma and Li, 2006; Jia and Zhang, 2015) and the L systems (Meng and Li, 2011; Zhang and Wang, 2011). Another important model for visualization is the weak chaos system (Zhang, 2004) upon which there are the derived mechanisms like quasi-regular system (Liu and Zhang, 2009), uniform stochastic web (Luo and Hong, 2014) and Hamiltonian function (Suyi and Leduo, 2009).…”

Section: Introductionmentioning

confidence: 99%

Textile pattern reconstruction and innovation using artificial neural network

Chen

Ya-feng

Hong

2019

IJCST

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Purpose The purpose of this paper is to advance a digital technology that is intended to bring about innovations on the existing textile patterns. Design/methodology/approach The pattern is deemed as a relation function between colors and positions which can be learnt by the artificial neural network (ANN). The outputs of the ANN are used for the reconstruction of the pattern and the innovation is performed by interceptors in the input/output layer. The ANN is carried out with one input layer, one output layer and several hidden layers, and the capacity of the architecture is adjusted by the scale of hidden layers to accommodate different function relations of the patterns. The training is conducted repeatedly on a sample set extracted from the pixels of the pattern image to minimize the error, and the chromatic outputs of the architecture are replaced to their origins so as to rebuild the pattern. Then, the interceptors are installed into the input and output layers to modulate the positions and the colors, and consequently the innovations are achieved on the geometric formation and color distribution of the pattern. Findings It has turned out that the precision of reconstruction is concerned with network scale, training epochs and color mode of the sample set. Four primary innovative effects including stripes, twisters, sandification and overprints have been qualified in terms of interceptors. Originality/value This study introduces ANN into textile pattern generation and provides a novel way to perform digital innovation of textile patterns.

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Fractal-based algorithmic design of Chinese ice-ray lattices

Rian

2022

Frontiers of Architectural Research

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Textile Pattern Design Based on IFS

Cited by 3 publications

References 3 publications

Modeling and refactoring the indigo patterns

Modeling and refactoring the indigo patterns

Textile pattern reconstruction and innovation using artificial neural network

Fractal-based algorithmic design of Chinese ice-ray lattices

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