• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.9
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• pp.1428-1435
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• 1989

In this paper we propose a new algorithm which extracts spatial arrangement information of texture elements in structural textures. In the proposed algorithm, by the use of projection information in several directions obtained from the texture image we can get two directions which determine the texture structure and the parallelogram grid which isolates texture elements. The isolated texture elements are analyzed and used to synthesize texture images. Computer simulation shows that the proposed method can extract proper spatial structure of the texture element even when the texture image is highly corrupted by additive noise.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.201-205
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• 2020

The structural texture is defined as a form which a texel is regularly repeated in the texture. Structural texture analysis/recognition has various industrial applications, such as automatic inspection of textiles, automatic testing of metal surfaces, and automatic analysis of micro images. In this paper, we propose a Convolution Neural Network (CNN) based system for structural texture analysis. The proposed method learns texles, which are components of textures to be classified. Then, this trained CNN recognizes a structural texture using a partial image obtained from input texture. The experiment shows the superiority of the proposed system.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.5
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• pp.533-544
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• 2003

The purpose of this study was to provide practical information to design woolen fabrics in terms of structural and surface characteristics, which produce texture images of fabrics. The relationship among structural, surface characteristics and texture images, and preference and purchase intention were analyzed. To evaluate the texture images of the fabrics subjectively, 7 rank＇s semantic differential scale questionnaires were developed with thirty adjective pairs. Blind and non-blind test were performed with 320 female subjects who were in their 20-30＇s. Commercially available 48 woolen fabrics were used as specimens. Results showed that five factors were obtained: classic, elegance, warmth, natural and casual. These factors were closely related to fiber type, weave type, fabric counts, and finishes.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.556-564
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• 2011

The purpose of this study was to analyze the effect of the structural properties, the color characteristics and the texture of sight and touch on the tactile preferences for the black fabrics. Male and female university students were asked to evaluate the texture of sight and touch and tactile preference to the nine different black fabrics which were selected on the basis of the previous research results. Data were analyzed by using frequency analysis, mean, factor analysis, t-test, F-test, correlation and regression analysis. The texture of sight and touch for black fabrics was classified into four factors: smoothness, bulkiness, extensibility, firmness. This texture of sight and touch factors showed a significant correlative relationship to the structural properties and color characteristics of the black fabrics. There were significant differences according to black fabrics on the point of texture of sight and touch. The velvet was evaluated the most smooth fabric, while the velvet and fake leather were evaluated the most bulky fabrics. Also, the jersey and lace fabrics were evaluated the most extensible fabrics, while the melton was evaluated as the most firm fabrics. There were significant correlative relationships not only between the structural properties and the texture of sight and touch but also between the color characteristics and the texture for black fabrics. Also, among the structural properties, the color characteristics and the texture of sight and touch of black fabrics, major variable factor of influencing on the tactile preference was turned out to be the texture of sight and touch.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.125-133
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• 2004

In this paper, we present a new method that combines the characteristics of edge in-formation and second-order neural networks for the classification of structural textures. The edges of a texture are extracted using an edge detection approach. From this edge information, classification features called second-order features are obtained. These features are fed into a second-order neural network for training and subsequent classification. It will be shown that the main disadvantage of using structural methods in texture classifications, namely, the difficulty of the extraction of texels, is overcome by the proposed method.

• Korean Journal of Human Ecology
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• v.18 no.1
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• pp.137-143
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• 2009

The purpose of this study is to examine the evaluation of texture image and preference according to the structural characteristics of silk fabric, and to analyze the effects of texture image and sensibility on the preference. 53 female subjects evaluated fabric image and sensibility of 17 specimens of white silk fabrics sold on the market with semantic differential scale. The data were analyzed through factor analysis, Pearson correlational coefficient and t-test using SPSS win 13.0. For the evaluation, structural characteristics such as fiber contents, weave type, weight and thickness were analyzed. Factor analysis showed that sensibilities were classified into 3 categories; 'surface property', 'weight', 'flexibility'. Fabric images were classified into 2 categories; 'elegance' and 'naturalness'. Statistically significant differences of structural characteristics on the texture image were observed. Weave type affected 'surface property' and fiber contents affected' flexibility'. Weight and weave type affected' elegance', too. The significant factors affecting preference were fabric image of 'elegance' and structural characteristics of 'weave type'. The results of this study showed that the most preferred silk fabric is smooth and soft satin weaved fabric with texture image of 'elegance'.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.11
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• pp.1516-1523
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• 2004

The purpose of this study were to analyze the effect of weft knit structural characteristics on the subjective texture and sensibility. For this, the material was knitted into 8 kinds of weft plain knit fabrics with four kinds of fiber components such as wool, acryl, rayon, and nylon, 3 steps of densities and 3 steps of twist numbers to ply two yarns. The data were analyzed by factor analysis, ANOVA and multidimensional scaling. From factor analysis, subjective textures were categorized as 'bulk/resilience', 'surface/density' and 'soft/drape', and subjective sensibilities were categorized as 'natural/comfortable', 'feminine/elegance' and 'stable/neat' Among the knit structural characteristics, the component of fibers and the density of fabrics were the important factors to give variations in texture and sensibility : In comparison with wool knit of medium density, the knit fabrics of other components and different densities each showed a unique texture and sensibility. But twist number to ply two yams had a few influence on subjective properties. As a result of MDS analysis, the texture and sensibility of plain weft knit fabrics was classified as 'thin-full', 'hard-soft', 'young-old' and 'warm-cool'.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1129-1144
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• 2020

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.675-683
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• 1987

In general, texture images with regular patterns can be described by using the standard texture model regularity vectors for their shape analysis. Early methods not only take much time but also have computational complexity in obtaining regularity vectors. The proposed some improved preprocessing algorithms for texture analysis. Finally, we showed the utility of the proposed method through texture synthesis by making use of the results of texture analysis.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.96-104
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• 1996

In this paper, we propose a new algorithm that obtains the surfac eorientation of the texture image using structural approaches. The proposed method showed that structural approaches can be effectively used in 3-D analysis of textures as well as description and segmentation without additional information. By examining fourier power spectrum of the texture image, we detemine the tilt of the textured surface. Then, 1-D projection information of the texture in the obtained tilt direction is used to compute the slant. Using the obtained information, we can compute the vanishing point, and rearrange the textured surface with lines converging to the vanishing point and lines perpendicular to the tilt direction. In the experimental results, we have ascertained the proposed algorithm can make a rpecise 3-D analysis of structural textures.