• The Journal of Natural Sciences
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• v.4
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• pp.191-220
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• 1991

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. For the boundaries, a binary image representing all the boundaries is created. For regions belonging to perceived constant intensity, only the mean intensity values need to be transmitted. The smooth and rough texture regions are modeled first using polynomial functions, so only the coefficients characterizing the polynomial functions need to be transmitted. The bounda-ries, the means and the polynomial functions are then each encoded using an errorless coding scheme. Good quality reconstructed images are obtained with about 0.08 to 0.3 bit per pixel for three different types of imagery ; a head and shoulder image with little texture variation, a complex image with many edges, and a natural outdoor image with highly textured areas.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.186-189
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• 2019

Image inpainting is an image processing technique that restores an image by naturally filling the empty or damaged regions in an image. In this paper, we present a new image inpainting technique that can suppress the generation of texture garbage which is one of the artifacts of existing exemplar-based image inpainting. Unlike the existing technique, only the stationary source patch is sampled as the exemplar patch based on the assumption of spatial stationarity of the texture. This prevents the texture garbage, which is an inconsistent piece of texture from being copied to the target region. Experimental results show that the texture synthesis using the proposed method produces more natural inpainting results than the existing method.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.989-991
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• 1995

In this paper, we present a new morphological texture spectrum approach to obtain a surface orientation using the variation of texture image caused by projective distortions. Under the assumption that the surface of texture image is smooth continuous, and specially plane or sphere, we apply the mathematical morphology and texture spectrum in order to compute the 3-D surface orientation. If the surface of texture image is plane, the surface orientation can be obtained through a simple procedure. If the surface of texture image is sphere, we find the centroids of texels, and may compute several major axes, their slopes, and vanishing points. Using the texture spectrum between the intersections of the vanishing points and the size of elements in each texels, we can find the surface orientation of texels on the sphere.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.2
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• pp.193-204
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• 2002

Many fashion forecasting companies propose the fashion colors in every season. Modern fashion consumer respond to fashionable trends with utmost sensitivity. Therefore to satisfy the consumer with an trendy image, the fashion design must be found first, as image matters, followed by an analysis of each design element's effect on the total image composition. In previous studies of fashion image, has been discussed the positive correlation between fashion design elements of color, fabric, and form as the central issue. In this thesis, two of the fashion design elements, color and fabric are simultaneously considered to classify the image of fabric in fashion. For the color variables, 10 hues are selected from Munsell's system of color notation, and 12 tones from PCCS color notation., which are currently used in the domestic fashion industry. Texture variables used in this survey are classified by luster, prominence-depression of surface, thickness, and density of fabric. Graduate students from 20 to 50 years old and the specialists in fashion companies participated in the survey. The results of this survey are as follows: 1. The fashion fabric image is classified as 5 main images: 'elegant', 'comfortable', 'characteristic', 'light'and 'simple'. 2. The influence of hue, tone and texture is significant to the fashion fabric image. Following colors, yellow-red, red hues and light grayish, dark grayish tones convey the elegant image. The texture property for the elegant image is luster, thin and low density. Properties of fabric conveying the comfortable image are yellow-red and green-yellow hue, soft, light tones, matte and high density. Furthermore, hue turned out to be a insignificant variables for the unique image, whereas dark grayish, grayish tone, luster and prominent texture convey a unique image. For light image, properties of fabric are blue-green, purple hues, light, bright tones with thin, low density texture. Properties of fabric conveying the simple image are blue-green, purple-blue, green-yellow hues, and strong, vivid tones, with luster and flat texture.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.178-184
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• 2023

AI image generation technology has become a popular research direction in the field of AI, which is widely used in the field of digital art and conceptual design, and can also be used in the process of 3D texture mapping. This paper introduces the production process of 3D texture mapping using AI image technology, and discusses whether it can be used as a new way of 3D texture mapping to enrich the 3D texture mapping production process. Two AI deep learning models, Stable Diffusion and Midjourney, were combined to generate high-quality AI textures. Finally, the lmage to material function of substance 3D Sampler was used to convert the AI-generated textures into PBR 3D texture maps. And applied in 3D environment. This study shows that 3D texture maps generated by AI image generation technology can be used in 3D environment, which not only has short production time and high production efficiency, but also has rich changes in map styles, which can be quickly adjusted and modified according to the design scheme. However, some AI texture maps need to be manually modified before they can be used. With the continuous development of AI technology, there will be great potential for further development and innovation of AI-generated image technology in the 3D content production process in the future.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.211-214
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• 2000

In this paper, we propose a regular-texture image retrieval approach relating In curvature. Maximum curvature and minimum curvature are computed from the query and each regular-texture image in the database. Seven features are computed from curvature characterizing statistical properties of the corresponding image. Each regular-texture image in the database is then represented as the seven CM (curvature measurement)-features. Query comparison and matching can be done using the corresponding CM-features. Experimental results on Brodatz texture show that the proposed approach is effective.

• Journal of Fashion Business
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• v.7 no.6
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• pp.85-91
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• 2003

Quick Response based Mass-Customization can be produced and distributed customized goods and services on mass basis in apparel e-business. Because consumers cannot touch and feel the apparel products in e-business, they tend to have the negative buying behavior. The purposes of this study were to analyze factors of texture image, and to investigate the differences of consumer sensibility on texture image of apparel products based on different marketing channels (on-line/off-line). Two types of questionnaires for on-line and off-line were used to assess consumer sensibility on apparel fabric. The 8 swatches were selected based on the previous literatures. 202 returned questionnaires for each type (on-line/off-line) were analyzed by t-test, mean and standard deviation with SPSS 10.0. The result of this study was showed that there were partially significant differences on consumer sensibility on texture image of apparel products between on-line and off-line. In case of corduroy, consumers perceived more high-class image under on-line than off-line. In case of taffeta, consumers perceived more thin and dense image under off-line (traditional marketing channel) than on-line (e-commerce). In case of denim, consumers perceived more thin and natural image under off-line than online. In case of organza, consumers perceived more natural image under on-line than off-line. In case of satin, consumers perceived more natural image under on-line than off-line. In case of chiffon, consumers perceived denser image under on-line than off-line. In case of velvet, consumers perceived thinner image, higher-class image, and more natural image of texture sensibility under on-line than off-line. In case of single jersey, consumers perceived higher-class image, and denser image of texture under on-line than off-line.

• Korean Journal of Computational Design and Engineering
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• v.9 no.4
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• pp.397-405
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• 2004

Texture mapping is the process of covering 3D models with texture images in order to increase the visual realism of the models. For proper mapping the coordinates of texture images need to coincide with those of the 3D models. When projective images from the camera are used as texture images, the texture image coordinates are defined by a camera calibration method. The texture image coordinates are determined by the relation between the coordinate systems of the camera image and the 3D object. With the projective camera images, the distortion effect caused by the camera lenses should be compensated in order to get accurate texture coordinates. The distortion effect problem has been dealt with iterative methods, where the camera calibration coefficients are computed first without considering the distortion effect and then modified properly. The methods not only cause to change the position of the camera perspective line in the image plane, but also require more control points. In this paper, a new iterative method is suggested for reducing the error by fixing the principal points in the image plane. The method considers the image distortion effect independently and fixes the values of correction coefficients, with which the distortion coefficients can be computed with fewer control points. It is shown that the camera distortion effects are compensated with fewer numbers of control points than the previous methods and the projective texture mapping results in more realistic image.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.133-138
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• 2010

In this paper, we make up a feature image including spatial properties and statistical properties on image, and format covariance matrices using region variance magnitudes. By using it to texture classification, this paper puts a proposal for tough texture classification way to illumination, noise and rotation. Also we offer a way to minimalize performance time of texture classification using integral image expressing middle image for fast calculation of region sum. To estimate performance evaluation of proposed way, this paper use a Brodatz texture image, and so conduct a noise addition and histogram specification and create rotation image. And then we conduct an experiment and get better performance over 96%.