• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.29-35
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• 2009

In this paper by using the image taken from the rear of the vehicle to effectively extract the license plate and how to recognize the characters appearing in the offer. How to existing research on the entire video by following the pre-edge (edge) images to obtain yijinhwa. Qualified heopeu in a binary image (Hough) to convert the horizontal and vertical lines to obtain, using the characteristics of the plates to extract the license plate area. The problem with this method, the processing time is so difficult to handle real-time status of irregular points, and visual contrast with yagangwan border does not appear in the plates to extract the license plate area is that it is not. In addition, the rear of the vehicle license plate area from images taken using the characteristics of the plates myeongamgap changes sutjapok in the area, background area and the number number area of the region confirmed the contrast of the car and identified the number and the number of 42 of distance to extract the license plate area. How to research, the existing damage to the border of the plate to fail to extract the license plate area, a matter of hours to resolve problems in real-time, practical application is processed. Chapter 100 as the results of the experiment the sample video image in a car that far experiment results automatically read license plates have been able to extract the license plate and failing to represent 13% of images, character recognition result of failing to represent the image was 0.4%

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.166-175
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• 2020

Recently, application fields for processing and using digital image contents in various forms and types are rapidly increasing. Since image content is high value-added content, the intellectual property rights of this content must be protected in order to activate the production and use of the digital image content. In this paper, we propose a deep learning based watermark embedding and extraction network. The proposed method is to maximize the robustness of the watermark against malicious/non-malicious attacks while preserving the invisibility of the host image. This network consists of a preprocessing network that changes the watermark to have the same resolution as the host image, a watermark embedding network that embeds watermark data while maintaining the resolution of the host image by three-dimensionally concatenating the changed host image and the watermark information, and a watermark extraction network that reduces the resolution and extracts watermarks. This network verifies the invisibility and robustness of the proposed method by experimenting with various pixel value change attacks and geometric attacks against various watermark data and host images with various resolutions, and shows that this method is universal and practical.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.768-782
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• 2010

Because of the different shooting position between multi-view cameras and the imperfect camera calibration, Illumination mismatches of multi-view video can happen. This variation can bring about the performance decrease of multi-view video coding(MVC) algorithm. A histogram matching algorithm can be applied to recompensate these inconsistencies in a prefiltering step. Once all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching, the coding efficiency of MVC is improved. However the histogram distribution can be different not only between neighboring views but also between sequential views on account of movements of camera angle and some objects, especially human. Therefore the histogram matching algorithm which references all frames in chose view is not appropriate for compensating the illumination differences of these sequence. Thus we propose new algorithms both the image classification algorithm which is applied two criteria to improve the correlation between inter-view frames and the histogram matching which references and matches with a group of pictures(GOP) as a unit to advance the correlation between successive frames. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with the conventional algorithms.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1369-1382
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• 2005

Vehicle segmentation, which extracts vehicle areas from road scenes, is one of the fundamental opera tions in lots of application areas including Intelligent Transportation Systems, and so on. We present a vehicle segmentation approach for still images captured from outdoor CCD cameras mounted on the supporting poles. We first divided the input image into a set of two-dimensional grids and then calculate the feature values of the edges for each grid. Through analyzing the feature values statistically, we can find the optimal rectangular grid area of the vehicle. Our preprocessing process calculates the statistics values for the feature values from background images captured under various circumstances. For a car image, we compare its feature values to the statistics values of the background images to finally decide whether the grid belongs to the vehicle area or not. We use dynamic programming technique to find the optimal rectangular gird area from these candidate grids. Based on the statistics analysis and global search techniques, our method is more systematic compared to the previous methods which usually rely on a kind of heuristics. Additionally, the statistics analysis achieves high reliability against noises and errors due to brightness changes, camera tremors, etc. Our prototype implementation performs the vehicle segmentation in average 0.150 second for each of $1280\times960$ car images. It shows $97.03\%$ of strictly successful cases from 270 images with various kinds of noises.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1352-1357
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• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.3-8
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• 2003

모든 SAR 영상에는 전자기파 간의 간섭으로 인한 스페클 잡영(speckle)이 존재하며, 이를 제거하는 것은 양질의 SAR 영상을 얻기 위한 필수적인 전처리 과정 중 하나라고 할 수 있다. 그러나 이러한 스페클 잡영을 제거하기 위하여 기존에 제안되었던 알고리즘은 잡영은 효과적으로 감소시키는 반면 경계선과 같은 영상의 고유 정보까지 함께 감소시키는 한계가 있었다. 따라서 본 연구에서는 SAR 영상의 경계선은 보존시키면서 영상으로부터 불필요한 잡영을 제거할 수 있는 알고리즘을 구현하고, 기존의 알고리즘과 비교하여 그 효율성을 평가하고자 한다. 영상의 통계적 특성에 근거하는 기존의 알고리즘과는 달리 웨이블렛 변환(Wavelet transform)으로 경계선 및 특징 정보의 여부를 판별한 후 평균 필터(mean filter)를 적용하는 경계선 보존(edge sharpening) 알고리즘은 경계 정보의 신뢰성을 향상시킬 수 있으며, 1차원 필터를 수평, 수직, 대각선, 역대각선 방향으로 적용함으로써 하나의 영상소를 중심으로 모든 방향에 대한 경계선 여부를 확인할 수 있는 장점이 있다. 본 연구에서는 512 × 512로 절취한 1-look SAR 영상에 대하여 창 크기 5 × 5의 경계선 보존 필터를 적용하고 동일영상에 대하여 기존의 Lee, Kuan, Frost 필터 등의 실험결과를 비교함으로써 그 적합성을 판단하고자 하였다. 실험결과에 대한 수치적인 평가는 ①정규화 평균을 이용하여 평균값의 보존 여부, ②편차 계수를 이용한 스페클 잡영의 제거 여부, ③경계선 보존지수(EPI)를 이용한 경계선의 보존 정도를 통해 이루어졌다. 본 연구의 실험결과를 통해 경계선 보존 필터는 평균값의 보존 여부 및 스페클 잡영 제거 정도에 있어 다른 필터들과 큰 차이가 없지만 경계선보존지수는 다른 필터들에 비하여 가장 우수함을 확인할 수 있었다.rbon 탐식효율을 조사한 결과 B, D 및 E 분획에서 유의적인 효과를 나타내었다. 이상의 결과를 종합해볼 때, ${\beta}$-glucan은 고용량일 때 직접적으로 또는 $IFN-{\gamma}$ 존재시에는 저용량에서도 복강 큰 포식세로를 활성화시킬 뿐 아니라, 탐식효율도 높임으로써 면역기능을 증진 시키는 것으로 나타났고, 그 효과는 crude ${\beta}$-glucan의 추출조건에 따라 달라지는 것을 알 수 있었다.eveloped. Design concepts and control methods of a new crane will be introduced in this paper.and momentum balance was applied to the fluid field of bundle. while the movement of′ individual material was taken into account. The constitutive model relating the surface force and the deformation of bundle was introduced by considering a representative prodedure that stands for the bundle movement. Then a fundamental equations system could be simplified considering a steady state of the process. On the basis of the simplified model, the simulation was performed and the results could be confirmed by the experiments under various conditions.뢰, 결속 등 다차원

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.101-112
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• 2005

As we perceive the world as 3-dimensional through our two eyes, we can extract 3-dimensional information from stereo images obtained from two or more cameras. Since stereo images have a large amount of data, with recent advances in digital video coding technology, efficient compression algorithms have been developed for stereo images. In order to compress stereo images and to obtain 3-D information such as depth, we find disparity vectors by using disparity estimation algorithm generally utilizing pixel differences between stereo pairs. However, it is not unusual to have stereo images having different intensity values for several reasons, such as incorrect control of the iris of each camera, disagreement of the foci of two cameras, orientation, position, and different characteristics of CCD (charge-coupled device) cameras, and so on. The intensity differences of stereo pairs often cause undesirable problems such as incorrect disparity vectors and consequent low coding efficiency. By compensating intensity differences between left and right images, we can obtain higher coding efficiency and hopefully reduce the perceptual burden of brain to combine different information incoming from two eyes. We propose several methods of intensity compensation such as local intensity compensation, global intensity compensation, and hierarchical intensity compensation as very simple and efficient preprocessing tool. Experimental results show that the proposed algerian provides significant improvement in coding efficiency.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.125-131
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• 2008

Hyperspectral imaging is used in a wide variety of research since the image is obtained with a wider wavelength range and more bands than multispectral imaging. However, there are limitations, namely that each band has a shorter wavelength range, the computation cost is increased in the case of numerous bands, and a high correlation between each band and noise bands exists. The previous analysis method does not produce ideal results due to these limitations. Therefore, in the case of using the hyperspectral image, image analysis after eliminating noise bands is more accurate and efficient. In this study, noise band elimination of the hyperspectral image preprocessing is highlighted, and we use fractal dimension for noise band elimination. The Triangular Prism Method is used, being the typical fractal dimension method of the curved surface. The fractal dimension of each band is calculated. We then apply the Continuum Removal method to normalize. A total of 35 bands are estimated by noise band with a threshold value that is obtained empirically. The hyperion hyperstpectral image collected on the EO-1 satellite is used in this study. The result delineates that noise bands of the hyperion image are able to be eliminated with the fractal dimension and Continuum Removal method.