• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.509-512
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• 2005

The aim of this work is to design a real-time reusable image enhancement architecture for video signals, based on a spatial processing of the video sequence. The VHDL hardware description language has been used in order to make possible a top-down design methodology. By adding proposed algorithms to the LPR(License Plate Recognition) system, the system is implemented with reliability and safety on a rainy day. Spartan-2E XC2s300E is used as implementation platforms for real-time system.

• Journal of the Semiconductor & Display Technology
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• v.14 no.1
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• pp.51-53
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• 2015

H.264/AVC is international video coding standard, which shows improved code and efficiency than the existing video standards. H.264/AVC proposes data partitioning method that considerably to be an effective layering technique which separates important addressing data from the residual data. UEP(Unequal Error Protection) turbo code of H.264/AVC uses retransmission system to get the UEP effectively. However, Data partitioning system of H.264/AVC is inefficient method in turbo code of H.264/AVC. Based on this observation, we propose the new UEP turbo code algorithm that reconstructs input sequence of turbo code without retransmission system.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.121-124
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• 2001

For efficient transmission of 3DTV video signals, it is necessary to eliminate the inherent redundancy between the stereo image pairs. Though disparity estimation provides a powerful tool for eliminating the redundancy, it is very time consuming. This paper presents a novel disparity estimation scheme based on the human visual property. The disparity vectors of image blocks spatially adjacent to the current block are used as initial guesses fur the disparity vector of the current block. In addition, mixed-resolution coding is applied to reduce the computational complexity of disparity estimation. Through computer simulations on a stereoscopic sequence we show that the proposed method gives rise .to visually pleasing results with much reduced computational complexity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.632-635
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• 2003

This paper describes a system fur tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• Journal of Korea Multimedia Society
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• v.7 no.9
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• pp.1240-1245
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• 2004

This paper presents the moving objects segmentation algorithms from the sequence images in the stationary backgrounds such as surveillance camera and video phone and so on. In this paper, the moving object area is extracted with proposed object searching algorithm and then moving object is segmented within the moving object area. Also the proposed algorithms have the robustness against noise problems and results show the proposed algorithm is able to efficiently segment and track the moving object area.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.546-552
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• 2016

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.352-355
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• 2011

본 논문에서는 깊이 맵의 재배열 과정을 통해서, 보다 개선된 영상을 합성하는 방법을 제안한다. 제안하는 방법은 전체 깊이 맵을 여러 그룹(Group)으로 나누고, 각각의 그룹에 서로 다른 가중치를 주어 가까운 물체에 좀 더 많은 깊이 값을 가질수 있도록 조절하였다. 깊이 맵 추정(Depth Estimation) 및 중간 시점 영상의 합성(View Synthesis)을 통하여 기존 방식과의 비교를 진행하였고 그 결과, 전체적인 비디오 시퀀스(Video Sequence)에 대한 PSNR은 유지하면서, 보다 시각적으로 자연스러운 영상을 얻을 수 있었다.

• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1465-1476
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• 2001

For the applications like video indexing, text understanding, and automatic captions localization system, real-time localization of captions is an essential task. This paper presents a algorithm for localization of captions in MPEG compression images based on I frame. In this algorithm, caption text regions are segmented from background images using their distinguishing texture characteristics and chrominance information. Unlike previously published algorithms which fully decompress the video sequence before extracting the text regions, this algorithm locates candidate caption text region directly in the DCT compressed domain.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.104-108
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• 1999

It is presented many effect that represented by implicated one image more than each images with a fragment meaning. ‘Mosaic’ is technique be applied at this situation. ‘Mosaic’ is created by complicated one new image which by multi image be eliminated overlap region. This research is development for mosaic system by multi image. The system is divided that shot segmentation and mosaic image creation. Shot segmentation divided that merge with respect to similarity images which video data of moving picture in sequence time and mosaic image creation is composed of one image with which all frames in segmented shot.