• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.197-206
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• 2010

High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.1-10
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• 2005

In this Paper, we propose a new block matching a1gorithm that extracts motion vectors from consecutive range data. The proposed method defines a matching metric that integrates intensity, hue, and range. Our algorithm begins matching with a small matching template. If the matching degree is not good enough, we slightly expand the size of a matching template and then repeat the matching process until our matching criterion is satisfied or the predetermined maximum size has been reached. As the iteration proceeds, we adaptively adjust weights of the matching metric by considering the importance of each feature. In the experiments, we show that our block matching approach can work as a promising solution by comparing the proposed method with previously known method in terms of performance.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.579-586
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• 2002

The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge information. In the edge descriptor, the edge information is the edge histogram derived from a gray-level value image. This paper proposes a new method which extracts color edge information from color images and a new approach for the contents-based image retrieval based on the color edge histogram. The poposed method and technique are applied to image retrieval of the e-catalogue. For the evaluation, the results of image retrieval using the proposed approach are compared with those of image retrieval using the edge descriptor by MPEG-7 and the statistics shows the efficiency of the proposed method. The proposed color edge model is made by combining the R,G,B channel components vectorially and by characterizing the vector norm of the edge map. The color edge histogram using the direction of the color edge model is subsequently used for the contents-based image retrieval.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.402-411
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• 2001

H.263 압축 방식은 실현하는데 여러 가지 문제가 있지만 그 중에서 그 대표적인 것은 인코딩 과정에서의 압축 시간이 오래 걸린다는 것이고 다른 한 가지는 과도한 압축률에 의한 복원된 이미지 화질 저하이다. 이 논문에서는 H.263에서의 압축 속도 향상과 복원 이미지의 화질 이미지의 화질 보상에 대한 두 가지 새로운 방법을 제안하였다. 압축 속도를 향상시키기 위해서 움직임 벡터를 찾는 알고리즘을 개선하여 새로운 4단계 탐색 알고리즘을 제안하였다. 또한 화질을 보상하기 위해 디코더에서 블록 아티팩을 제거하고 복원 이미지를 선명하게 하는 알고리즘을 제안하였다. 여기서 화질 보상은 원본 이미지와 동일하게 만드는 것이 아니라 인간이 더 좋은 영상으로 인식하도록 하는 걸 목적으로 한다. 우리가 제한한 알고리즘에 의해서 압축 속도는 초당 2.5에서 17 프레임으로 증가하였고 블록 아티팩을 제거하고 명암 대비를 높임으로써 보기 좋은 영상을 제공하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.557-560
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• 2010

고성능의 보급형 디지털 영상장비와 레이저프린터의 보급으로 인해 인쇄물의 불법적인 위변조가 사회적 문제로 대두되고 있고, 관련 범죄 또한 증가하는 추세이다. 이에 따라 디지털 포렌식 기술에 기반한 촬영 및 인쇄기기 식별 기술들이 연구되고 있다. 본 논문에서는 인쇄기기별 특성에서 기인하는 인쇄물의 노이즈 특성을 이용해 인쇄기기를 판별하는 알고리즘을 제안한다. 이산 웨이블릿 변환과 위너 필터를 이용한 노이즈 특성 추출 방법을 설명하고, 추출된 노이즈 특성에서 명암도 동시발생 행렬을 계산하고 왜도, 첨도, 공분산, 상관계수의 특징을 추출하였다. 추출한 특징을 서포트 벡터 머신에 적용하여 디지털 인쇄기기의 제조사와 모델을 판별하였다. 제안한 알고리즘의 성능을 분석하기 위하여 7대 프린터에서 각 371장씩 출력된 총 2,597장 이미지로 실험하였고, 제안한 알고리즘이 기존 방법에 비하여 높은 정확률을 나타냄을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1140-1151
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• 2003

This paper proposes a face recognition method based on modified Otsu's binarization and Hu moment. Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. As the proposed modified Otsu's binarization computes other thresholds from conventional Otsu's binarization, namely we create two binary images, we can extract higher dimensional feature vector. Here the feature vector has properties of robustness to brightness and contrast changes because the proposed method is based on Otsu's binarization. And our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. In the perspective of brightness, contrast, scale, rotation, and translation changes, experimental results with Olivetti Research Laboratory (ORL) database and the AR database showed that average recognition rates of conventional well-known principal component analysis (PCA) are 93.2% and 81.4%, respectively. Meanwhile, the proposed method for the same databases has superior performance of the average recognition rates of 93.2% and 81.4%, respectively.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.1-8
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• 2009

There are a lot of influences, such as location of camera, luminosity, brightness, and direction of light, which affect the performance of 2-dimensional image recognition. This paper suggests an adaptive method for face-image recognition in noisy environments using evolvable filtering and feature extraction which uses one sample image from camera. This suggested method consists of two main parts. One is the environmental-adjustment module which determines optimum sets of filters, filter parameters, and dimensions of features by using "steady state genetic algorithm". The other another part is for face recognition module which performs recognition of face-image using the previous results. In the processing, we used Gabor wavelet for extracting features in the images and k-Nearest Neighbor method for the classification. For testing of the adaptive face recognition method, we tested the adaptive method in the brightness noise, in the impulse noise and in the composite noise and verified that the adaptive method protects face recognition-rate's rapidly decrease which can be occurred generally in the noisy environments.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.15-25
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• 2002

This paper proposes a vehicle license plate recognition system, which has relatively a simple structure and is highly tolerant of noise, by using the DCT(Discrete Cosine Transform) coefficients extracted from the character region of a license plate and the LVQ(Learning Vector Quantization) neural network. The image of a license plate is taken from a captured vehicle image based on RGB color information, and the character region is derived by the histogram of the license plate and the relative position of individual characters in the plate. The feature vector obtained by the DCT of extracted character region is utilized as an input to the LVQ neural classifier fur the recognition process. In the experiment, 109 vehicle images captured under various types of circumstances were tested with the proposed method, and the relatively high extraction rate of license plates and recognition rate were achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1661-1666
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• 2010

Due to light condition or shadow around camera, acquired image sequence is often degraded by intensity fluctuation. This artifact is called luminance flicker. As the luminance flicker corrupts the performance of motion estimation or object detection, it should be corrected before further processing. In this paper, we analyze the flicker generation model and propose the new algorithm for flicker reduction. The proposed algorithm considers gain and offset parameter separately, and stabilizes the luminance fluctuation based on these parameters. We show the performance of the proposed method by testing on the sequence with artificially added luminance flicker and real sequence with object motion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2478-2484
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• 2010

In this paper, real-time detection methods for Panorama system Key-Points offers. A recent study in PANORAMA system real-time area navigation or DVR to apply such research has recently been actively. The detection of the Key-Point is the most important elements that make up a Panorama system. Not affected by contrast, scale, Orientation must be detected Key-Point. Existing research methods are difficult to use in real-time Because it takes a lot of computation time. Therefore, this paper propose BLOG(BitRate Laplacian Of Gaussian)method for faster time Key-Point Detecting and Through various experiments to detect the Speed, Computation, detection performance is compared against.