• Journal of Korea Multimedia Society
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• v.9 no.10
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• pp.1266-1275
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• 2006

In this paper, we propose a real-lime face detection and tracking algorithm using AdaBoost(Adaptive Boosting) algorithm. The proposed algorithm consists of two levels such as the face detection and the face tracking. First, the face detection used the eight-wavelet feature models which ate very simple. Each feature model applied to variable size and position, and then create initial feature set. The intial feature set and the training images which were consisted of face images, non-face images used the AdaBoost algorithm. The basic principal of the AdaBoost algorithm is to create final strong classifier joining linearly weak classifiers. In the training of the AdaBoost algorithm, we propose SAT(Summed-Area Table) method. Face tracking becomes accomplished at real-time using the position information and the size information of detected face, and it is extended view region dynamically using the fan-Tilt camera. We are setting to move center of the detected face to center of the Image. The experiment results were amply satisfied with the computational efficiency and the detection rates. In real-time application using Pan-Tilt camera, the detecter runs at about 12 frames per second.

• Journal of Korea Multimedia Society
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• v.17 no.11
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• pp.1325-1334
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• 2014

Recently game addiction of young people has become a social issue. Therefore, many studies, mostly surveys, have been conducted to diagnose game addiction. In this paper, we suggest how to distinguish levels of addiction based on EEG. To this end, we first classify four groups by the degrees of addiction to internet games (High-risk group, Vigilance group, Normal group, Good-user group) using CSG (Comprehensive Scale for Assessing Game Behavior) and then measure their Event Related Potential(ERP) in the Go/NoGo Task. Specifically, we measure the signals of P300, N400 and N200 from the channels of the NoGo stimulus and Go stimulus. In addition, we extract distinct features from the discrete wavelet transform of the EEG signal and use these features to distinguish the degrees of addiction to internet games. The experiments in this study show that High-risk and Vigilance group exhibit lower Go-N200 amplitude of Fz channel than Normal and Good-user groups. In Go-P300 and NoGo-P300 of Fz channel, High-risk and Vigilance groups exhibit higher amplitude than Normal and Good-user group. In Go-N400 and NoGo-N400 of Pz channel, High-risk and Vigilance group exhibit lower amplitude than Normal and Good-user group. The test after the learning study of the extracted characteristics of each frequency band from the EEG signal showed 85% classification accuracy.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.151-157
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• 2012

Recently, owing to the technology advances in computer science and image processing, age of face classification have become prevalent topics. It is difficult to estimate age of facial shape with statistical figures because facial shape of the person should change due to not only biological gene but also personal habits. In this paper, we proposed a robust age of face classification method by using Gabor feature and fuzzy support vector machine(SVM). Gabor wavelet function is used for extracting facial feature vector and in order to solve the intrinsic age ambiguity problem, a fuzzy support vector machine(FSVM) is introduced. By utilizing the FSVM age membership functions is defined. Some experiments have conducted to testify the proposed approach and experimental results showed that the proposed method can achieve better age of face classification precision.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.5
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• pp.985-993
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• 2001

Neural networks are shown to be effective in being able to distinguish incomplete penetration-like weld defects by directly analyzing the plasma which is generated on each impingement of the laser on the materials. The performance is similar to that of existing methods based on extracted feature parameters. In each case around 93% of the defects in a database derived from 100 artificially produced defects of known types can be placed into one of two classes: incomplete penetration and bubbling. The present method based on classification using plasma is faster, and the speed is sufficient to allow on-line classification during data collection.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.577-586
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• 2005

Many algorithms have been proposed for (ace recognition that is one of the most successful applications in image processing, pattern recognition and computer vision fields. Research for what kind of attribute of face that make harder or easier recognizing the target is going on recently. In flus paper, we propose method to improve recognition performance using relevance of face data and applied algorithms, because recognition performance of each algorithm according to facial attribute(illumination and expression) is change. In the experiment, we use n-tuple classifier, PCA and Gabor wavelet as recognition algorithm. And we propose three vectorization methods. First of all, we estimate the fitnesses of three recognition algorithms about each cluster after clustering the test data using k-means algorithm then we compose new clusters by integrating clusters that select same algorithm. We estimate similarity about a new cluster of test data and then we recognize the target using the nearest cluster. As a result, we can observe that the recognition performance has improved than the performance by a single algorithm without clustering.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2271-2280
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• 1999

In this paper, a variable motion estimation scheme based on HBMA(Hierarchical Block Matching Algorithm) to improve the performance and to reduce heavy computational and transmission load, is presented. The proposed algorithm is composed of four steps. First, block activity for each block is defined using the edge information of differential image between two sequential images, and then average block activity of the present image is found by taking the mean of block activity. Secondly, camera pan compensation is carried out, according to the average activity of the image, in the hierarchical pyramid structure constructed by wavelet transform. Next, the LUT classifying each block into one among Moving, No Moving, Semi-Moving Block according to the block activity compensated camera pan is obtained. Finally, as varying the block size and adaptively selecting the initial search layer and the search range referring to LUT, the proposed variable HBMA can effectively carries out fast motion estimation in the hierarchical pyramid structure. The cost function needed above-mentioned each step is only the block activity defined by the edge information of the differential image in the sequential images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.64-71
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• 2003

In this paper, we proposed an algorithm that shorten coding time maintaining image quality in JPEG2000, which is the standard, of still image compression. This method encodes only the bit plane selected as appropriate truncation point for output bitstream, obtained from estimation of frequency distribution for whole image. Wavelet characterized by multi-resolution has vertical, horizontal, and diagonal frequency components for each resolution. The frequency interrelation addressed above is maintained thorough whole level of resolution and represents the unique frequency characteristics for input image. Thus, using the frequency relation at highest level, we can pick the truncation point for the compression time decrease by estimating code bits at encoding each code block. Also, we reduced the encoding time using simply down sampling instead of low-pass filtering at low-levels which are not encoded in color component of lower energy than luminance component. From the proposed algorithm, we can reduce about 15~36% of encoding time maintaining PSNR 30$\pm$0.5㏈.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.864-873
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• 2001

In this paper, a new approach to building a registration authority for issuing PKI-based certificates is presented to make the process of identifying an individual more secure and reliable by utilizing human iris recognition technology. The tasks of the proposed system associated with the manipulation of irises except for the general functions of registration authorities can be categorized into three modules, the acquisition of iris images, the registration of iris information, and the verification of users by means of iris patterns. The information among the three modules is safely exchanged through encryption and decryption with a symmetric cryptographic method. As a feature extraction method for a given iris image, a wavelet transform is applied to represent a feature vector with a small dimension of information obtained by subsampling an image corresponding to lower frequency bands successively without loss of information. Through the experiments on human iris recognition technology we proposed and applied to the registration authority, the potential of biometric technology in various applications is confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.155-160
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• 2010

In this paper, we proposed a forensic mark algorithm which can embed the distributor's information at each distribution step to trace the illegal distribution path. For this purpose, the algorithm has to have the high capacity payload for embedding the copyright and user information at each step, and the embedded information at a step should not interfere with the information at other step. The proposed algorithm can trace the multilevel distribution because the forensic mark is generated by digital hologram and embedded in the DWT-SVD domain. For the high capacity embedding, the off-axis hologram is generated from the forensic mark and the hologram is embedded in the HL, LH, HH bands of the DWT to reduce the signal interference. The SVD which is applied the holographic signal enhanced the detection performance and the safety of the forensic mark algorithm. As the test results, this algorithm was able to embed 128bits information for the copyright and user information at each step. In this paper, we can embed total 384bits information for 3 steps and the algorithm is also robust to the JPEG compression.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.1-8
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• 2003

Various methods have been studying to maintain and apply the multimedia inform abruptly increasing over all social fields, in recent years. For retrieval of still images, we is implemented content-based image retrieval system in this paper that make possible to retrieve similar objects from image database after segmenting query object from background if user request query. Query image is processed median filtering to remove noise first and then object edge is detected it by canny edge detection. And query object is segmented from background by using convex hull. Similarity value can be obtained by means of histogram intersection with database image after securing color histogram from segmented image. Also segmented image is processed gray convert and wavelet transform to extract spacial gray distribution and texture feature. After that, Similarity value can be obtained by means of banded autocorrelogram and energy. Final similar image can be retrieved by adding upper similarity values that it make possible to not only robust in background but also better correct object retrieval by using object segmentation method.