• 자연과학논문집
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• 제4권
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• pp.191-220
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• 1991

Human Visual System(HVS)의 특성과 image의 textural regions의 roughness을 이용하여 image segmentation을 행하여 high compression에서도 고화질을 나타내는 새로운 image coder를 이 논문에서 논한다. 제안된 image coder는 constant segments를 가진 segmentation-based image coding technique의 문제들을 다음과 같은 방법론을 제안함으로써 해결하였다. Image를 HVS으로 보았을 때 degree of roughness에 관하여 textually homogeneous regions으로 segmentation하였다. Fractal dimension을 roughness of textural regions을 측정하기 위하여 사용하였다. Segmentation은 fractal dimension을 thresholding하여 textural regions이 three texture classes로 분류하였다(perceived constant intensity, smooth texture, and rough texture). High compression을 가지는 고질화의 image coder는 각각의 segment boundary와 각각의 texture class에 효율적인 coding technique를 적용 함으로 얻었다.

• 방송공학회논문지
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• 제24권1호
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• pp.186-189
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• 2019

영상 인페인팅(image inpainting)이란 입력 영상에 훼손되거나 빈 영영이 존재할 경우 이 영역을 자연스럽게 채워 영상을 복원해내는 영상처리 기법이다. 본 논문에서는 기존의 예제 기반(exemplar-based) 영상 인페인팅의 단점 중 하나인 텍스쳐 가비지(texture garbage)의 생성을 억제할 수 있는 새로운 영상 인페인팅 기법을 제시한다. 기존 기법과 달리 영상의 텍스쳐는 통계적으로 정적(stationary)이라는 가정 하에 정적인 소스 패치만을 후보 패치로 샘플링 한다. 이를 통해 주변 신호와 일치하지 않는 신호인 텍스쳐 가비지가 타겟 영역에 복사되는 것을 방지할 수 있다. 실험을 통해 제안 기법을 이용한 텍스쳐 합성이 기존 기법에 비해 더욱 자연스러운 영상 인페인팅 결과를 생성함을 확인한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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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.

• 한국의류학회지
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• 제26권2호
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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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• 제15권4호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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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.

• 패션비즈니스
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• 제7권6호
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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.

• 한국CDE학회논문집
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• 제9권4호
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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.

• 한국인터넷방송통신학회논문지
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• 제10권5호
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• pp.133-138
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• 2010

본 논문에서는 이미지에 대한 공간 특성(Spatial properties) 및 통계적 특성(Statistical properties)을 포함한 특징이미지를 구성하고, 지역 분산 크기를 이용한 공분산 행렬을 생성하여 텍스쳐 분류에 이용함으로서 조도(illumination) 및 노이즈(Noise) 그리고 회전(Rotation)에 강인한 텍스쳐 분류 방법을 제안한다. 또한 영역 합계의 빠른 연산을 위해 사용된 중간 이미지 표현인 적분 이미지(Integral Image)를 이용함으로서 텍스쳐 검출 프로세스의 수행 시간을 최소화 하는 방법을 제공한다. 제안한 방법의 성능 평가를 위해 브로다츠(Brodatz) 질감 이미지를 이용하여 잡음 추가 및 히스토그램 명세화 그리고 회전 이미지를 생성하여 실험하였으며, 96% 이상의 성능을 얻을 수 있었다.