• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.116-129
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• 2001

To improve the performance of the inverse halftoning, smoothing masks should be designed optimally by using the accurate information of halftone cells. In this thesis, the method of energy minimization is so defined as to determine the exact information of halftone cell. A heuristic search method is proposed to obtain efficiently the parameters of halftone cells which determine the minimum energy. A halftone-peak modeling method with several functions is proposed and used to get initial values of the parameters. The dimension decomposition technique is also adopted to speed up the search process of energy minimization. Several experiments show that the proposed method extracts correct location of the seed pixel of the halftone cell and the extracted information of the halftone cell can be used to get more exactly smoothed color images. The proposed method can be applied to extract the texture patterns, to separate channel images of a scanned color halftone image, and to extract the moire area in an image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.1-12
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• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.47-54
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• 2011

This paper describes the method to reduce the time-cost for depth estimation of an image by learning, on the basis of the Genetic Algorithm, the image's features. The depth information is estimated from the relationship among features such as the energy value of an image and the gradient of the texture etc. The estimation-time increases due to the large dimension of an image's features used in the estimating process. And the use of the features without consideration of their importance can have an adverse effect on the performance. So, it is necessary to reduce the dimension of an image's features based on the significance of each feature. Evaluation of the method proposed in this paper using benchmark data provided by Stanford University found that the time-cost for feature extraction and depth estimation improved by 60% and the accuracy was increased by 0.4% on average and up to 2.5%.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.12
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• pp.665-682
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• 2007

Line drawing is a widely used style in non-photorealistic rendering because it generates expressive descriptions of object shapes with a set of strokes. Although various techniques for line drawing of 3D objects have been developed, line drawing of 2D images has attracted little attention despite interesting applications, such as image stylization. This paper presents a robust and effective technique for generating line drawings from 2D images. The algorithm consists of three parts; filtering, linking, and stylization. In the filtering process, it constructs a likelihood function that estimates possible positions of lines in an image. In the linking process, line strokes are extracted from the likelihood function using clustering and graph search algorithms. In the stylization process, it generates various kinds of line drawings by applying curve fitting and texture mapping to the extracted line strokes. Experimental results demonstrate that the proposed technique can be applied to the various kinds of line drawings from 2D images with detail control.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1094-1107
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• 2011

We can use panorama systems or image based modeling systems when we want to make 3D space model and look around result. Panorama systems make 3D model to stitch images and map cylinder or cube. The structure of 3D model, made by panorama system, is not same as structure of a real room, so user can't infer a real structure. Typically, Image based modeling systems work on a desktop computer. That makes it difficult to reconstruct 3D model in real time and take long time for processing. In this paper, we propose a 3D panorama modeling system that uses images on a mobile device. This system reconstructs a 3D space model, similar with a real room in real time, from multiple images captured part of rooms. Using this system, user can reconstruct various shape of space and look around a 3D space model.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.23-28
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• 2017

Shape from focus (SFF) is a technique used to reconstruct 3D shape of objects from a sequence of images obtained at different focus settings of the lens. In this paper, a new shape from focus method for 3D reconstruction of microscopic objects is described, which is based on gradient operator in Mathematical Morphology. Conventionally, in SFF methods, a single focus measure is used for measuring the focus quality. Due to the complex shape and texture of microscopic objects, single measure based operators are not sufficient, so we propose morphological operators with multi-structuring elements for computing the focus values. Finally, an optimal focus measure is obtained by combining the response of all focus measures. The experimental results showed that the proposed algorithm has provided more accurate depth maps than the existing methods in terms of three-dimensional shape recovery.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.737-744
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• 2005

In this paper, we propose a three-level color clustering algerian for the binarization of text regions extracted from natural scene images. The proposed algorithm consists of three phases of color segmentation. First, the ordinary images in which the texts are well separated from the background, are binarized. Then, in the second phase, the input image is passed through a high pass filter to deal with those affected by natural or artificial light. Finally, the image Is passed through a low pass filter to deal with the texture in texts and/or background. We have shown that the proposed algorithm is more effective used gray-information binarization algorithm. To evaluate the effectiveness of the proposed algorithm we use a commercial OCR software ARMI 6.0 to observe the recognition accuracies on the binarized images. The experimental results on word and character recognition show that the proposed approach is more accurate than conventional methods by over $35\%$.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1319-1324
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• 2006

최근 다양한 분야(건축, 디자인, 영화관)에서 활용되는 디스플레이 기술들은 대체로 평면에 투영하는 프로젝션 기술을 사용하고 있다. 예외적으로 평면이 아닌 곡면에 투영하는 경우도 있었으나, 기술적인 제약으로 왜곡된 영상을 보정하여 사용하는 경우는 드문 상태이다. 그리고 상호작용의 경우는 기계적 장치에 의존한 초보적 형태가 주류를 이루고 있다. 본 논문에서는 프로젝션 기술 중에서 원통형 곡면상으로의 투영 기법과 모션인식을 반영한 상호작용 기법에 대해서 논하고자 한다. 3D 영상을 곡면에 왜곡 없이 투영하기 위하여, 본 논문에서는 '2-pass 렌더링' 기법을 이용하였다. 이 기법에서는 현재 렌더링 된 영상을 텍스쳐로 저장한 다음 원통형 물체에 매핑시켜 곡면에 적합한 영상으로 보정한다. 그리고 기계적 장치에 의존하지 않는 상호작용을 위해, 카메라를 통하여 실시간으로 사용자 정보(위치, 방향 값)를 입력 받아 원통형 스크린과 매칭되는 좌표 값을 계산한다. 위와 같은 기법들을 구현하기 위한 실험으로 미디어 아트 작품을 제작하였으며, 투영과 상호작용에 관한 알고리즘을 작품에 적용하였다. 이 작품은 하나의 프로젝터를 사용하여 1/4 원통형 곡면으로의 투영과 상호작용을 수행하였다. 본 연구의 결과는 미디어 아트 작품의 프로젝션 모듈로 사용 될 수 있으며, 공연장 건축, 실내디자인, 체감형 인터랙티브 게임, 가상현실 영화관 등 다양한 분야에 적용 될 수 있다.