• Journal of Korea Game Society
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• v.11 no.2
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• pp.115-129
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• 2011

In this paper, we will introduce some method about retrieving suitable images to game or adjusting game difficulty in enjoying some contents like mini-game. It will use the technology about extracting color and texture features in content-based image retrieval in image processing. So in card game, it select card image automatically. And by controlling seed image number, we can adjusting game difficulty. Through the experiment, it shows that our image retrieval method can retrieve more useful images that can be used in game than others.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.85-95
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• 2005

This study proposes a method of feature point extraction using scale-space filtering and a feature point tracking algorithm based on a texturedness similarity comparison, With well-defined operators one can select a scale parameter for feature point extraction; this affects the selection and localization of the feature points and also the performance of the tracking algorithm. This study suggests a feature extraction method using scale-space filtering, With a change in the camera's point of view or movement of an object in sequential images, the window of a feature point will have an affine transform. Traditionally, it is difficult to measure the similarity between correspondence points, and tracking errors often occur. This study also suggests a tracking algorithm that expands Shi-Tomasi-Kanade's tracking algorithm with texturedness similarity.

• 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%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.11-24
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• 2008

Region-based image retrieval techniques, which divide image into similar regions having similar characteristics and examine similarities among divided regions, were proposed to support an efficient low-dimensional color indexing scheme. However, color image segmentation techniques are required additionally. The problem of segmentation is difficult because of a large variety of color and texture. It is known to be difficult to identify image regions containing the same color-texture pattern in natural scenes. In this paper we propose an automatic color image segmentation algorithm. The colors in each image are first quantized to reduce the number of colors. The gray level of image representing the outline edge of image is constructed in terms of Fisher's multi-class linear discriminant on quantized images. The gray level of image is transformed into a binary edge image. The edge showing the outline of the binary edge image links to the nearest edge if disconnected. Finally, the final segmentation image is obtained by merging similar regions. In this paper we design and implement a region-based image retrieval system using the proposed segmentation. A variety of experiments show that the proposed segmentation scheme provides good segmentation results on a variety of images.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.655-657
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• 2016

우리는 빛의 정도에 따라 사진의 밝기와 채도, 대비를 보정하고 더 나아가 역광을 보정하는 기술을 구현하였다. 색감과 질감의 경우, 기존과는 다른 방법으로 질감과 색감을 추출했다. 역광보정은 자동이나 수동으로 할 수 있는데, 수동으로 역광보정을 적용하기 위해서는 먼저 관심영역을 지정해 주어야한다. 관심영역은 사진 속 원하는 부분의 윤곽선을 이어줌으로써 선택한다. 우리는 자석 올가미를 통하여 섬세한 선택을 가능하게 하였다. 기존 올가미 기능은 시작점과 끝점을 일치시켜 주어야 하는 단점이 있었으나 제안하는 올가미 기능은 시작점과 끝점을 일치시키지 않아도 관심영역을 선택할 수 있는 장점이 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.233-236
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• 2001

멀티미디어 데이터의 증가와 마이닝 기술의 발전으로 인해 멀티미디어 마이닝에 대한 관심이 증가하고 있다. 본 논문에서는 특성국지화를 이용한 내용기반의 정보검색 기술과 다차원 데이터큐브 구축기술을 통해 멀티미디어 데이터에서 연관규칙을 찾아내는 멀티미디어 데이터마이닝 시스템 프로토타입을 제안한다. 특히 멀티미디어 데이터의 칼라, 질감 등 거시적인 이미지 성분 대신 이미지의 영역성과 유사성을 이용한 특성국지화방법을 이용하여 이미지를 분할함으로써 방대한 데이타에서 효과적인 내용기반의 정의 검색을 시행하고 검색한 벡터를 메타데이타로 한 데이스베이스를 구축한다. 그리고 데이터베이스에서 데이터간 연관규칙을 찾아내어 지식을 마이닝하는데 효과적인 다차원 데이터큐브를 구축하고 여기에 연관규칙 검색 알고리즘을 적용한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.288-292
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• 2000

본 논문은 컴퓨터 그래픽 이미지의 데이터를 디지털화 하는 과정에서 Aliasing으로 인하여 손실된 Pixel 정보에 대해 동적 블럭으로 분할 연산하고 벡터 양자화, Gaussian 함수를 이용하여 손실된 정보들을 보간하여 해상도가 높아진 영상을 얻을 수 있는 새로운 Anti-Aliasing 알고리즘을 제시한다. Anti-Aliasing의 효과를 더욱 시각적으로 분별 할 수 있도록 하기 위해 Gray 레벨의 이미지로 실험을 하였고, 현재 Graphic을 지원하는 하드웨어 구조의 PC 기반에 변화 없이 적용할 수 있고, 이미지의 질감을 더욱 부드럽게 향상 시킬수가 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Science of Emotion and Sensibility
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• v.19 no.3
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• pp.27-42
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• 2016

The purposes of this study were to investigate the emotional consumption propensity and the preferences for sensibility factors of fabrics(color tone, pattern and texture image), and to analyse the differences according to demographic variables and relationships between emotional consumption propensity and preferences for sensibility factors of fabrics, focusing on male and female consumers in 20's, 30's and 40's. The emotional consumption propensity were classified into symbolic consumption propensity, individual consumption propensity, aesthetic consumption propensity and hedonic consumption propensity. The subjects attached great importance in the order of aesthetic consumption propensity, individual consumption propensity and symbolic consumption propensity. Those factors of emotional consumption propensity showed partially significant difference according to demographic variables. Female consumers preferred various color tones than men did, and preference for light color tone showed significant differences according to gender and occupation of consumers. The preferences for floral pattern showed significant difference according to gender, age, education, occupation and marital status of consumers. The factors of the texture images for the fabrics showed partially significant difference according to demographic variables except education of consumers. There were almost significant relationships between emotional consumption propensity and the preferences for sensibility factors for fabrics.