• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.850-858
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• 2013

In this paper, we propose a method of frame synchronization for stereoscopic 3D video to solve the viewing problem caused by synchronization errors between a left video and a right video using the temporal frame difference image depending on the movement of objects. Firstly, we compute two temporal frame difference images from the left video and the right video which are corrected the vertical parallax between two videos using rectification, and calculate two horizontal projection profiles of two temporal frame difference images. Then, we find a pair of synchronized frames of the two videos by measuring the mean of absolute difference (MAD) of two horizontal projection profiles. Experimental results show that the proposed method can be used for stereoscopic 3D video, and is robust against Gaussian noise and video compression by H.264/AVC.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.141-147
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• 1998

Design sensitivity analysis of a vehicle system is an essential tool for design optimization and trade-off studies. Most optimization algorithms require the derivatives of cost and constraint function with respect to design in order to calculate the next improved design. This paper presents an efficient algorithm application for the design sensitivity analysis, using the direct differentiation method. A mounting area of suspension that welded on chassis frame is analyzed to show the validity and the efficiency of the proposed method. A mounting area of suspension that welded on chassis frame is analyzed to show the validity and the efficiency of the proposed method.

• The Journal of Information Systems
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• v.14 no.3
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• pp.117-122
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• 2005

This paper presents a method for detecting scene changes in video sequences, in which the $chi^{2}$-test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted $chi^{2}$-test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.4
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• pp.11-18
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• 2007

A moving object detection algorithm for surveillance video is here proposed which employs background initialization based on two-stage median filtering and a background updating method based on state transition diagram. In the background initialization, the spatiotemporal similarity is measured in the subinterval. From the accumulated difference between the base frame and the other frames in a subinterval, the regions affected by moving objects are located. The median is applied over the subsequence in the subinterval in which regions share similarity. The outputs from each subinterval are filtered by a two-stage median filter. The background of every frame is updated by the suggested state transition diagram The object is detected by the difference between the current frame and the updated background. The proposed method showed good results even for busy, crowded sequences which included moving objects from the first frame.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.147-154
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• 2008

First, this paper compute difference value between frames using the composed method of $X^2$ histogram and color histogram and the normalization. Next, cluster representative frame was decided by using the clustering for distance and the k-mean grouping. Finally, representative frame of group was decided by using the likelihood ratio. Proposed method can be known by experiment as outstanding of detection rather than other methods, due to computing of difference value, clustering and grouping, and detecting of representative frame.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.413-416
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• 2003

This paper proposes an adaptive background image generation method based on the frame difference for traffic monitoring. The performance of the conventional method is limited when there are more vehicles due to traffic Jam. To improve on this, we use frame differencing to separate vehicles from background in frame differencing, we adopt selective approach by using part of the image not considered as vehicle fer extraction of background. The proposed method generates background more efficiently than conventional methods even in the presence of heavy traffic.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.876-890
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• 2012

In this paper, we investigated the performances of the distributed video codec with adaptive Gray code to apply for frame-difference signal. That is, the best cases and the worst cases were analyzed and compared by considering the statistical characteristics of the frame difference signal in view of the Gray code allocation. Through computer simulations, if 9-bit data for frame difference signal is generated for luminance signal with 8-bit definition and so n-bit is allocated to the quantized coefficient, we were able to find the best method to reduce the virtual channel noise by adding $256+2^{9-n-1}$ to the frame difference signal. Through computer simulation with test video sequences, it was shown that the performance difference between the best cases and the worst cases is larger than about 1.5dB at same rate. It is expected that the results in this paper are applicable for the transform-domain scheme as well as the pixel-domain scheme.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.54-57
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• 2000

In this paper, we propose a new interpolation method by using the motion between two moving image frames. In the proposed method, the movement is detected by using neighborhood pixels of target pixel in the past frame and the present frame. Then, H-shaped pseudomedian filter (below HPMED) is used for the still part of the image and Delta-shaped interpolation filter (below $\Delta$-shaped) for used in the moving part of the image. We detect the movement by comparing the differences between pixels in 4${\times}$5 window of the past frame and the present frame; the difference has a critical value. We simultaneously accomplish checking PSNR(peak signal noise ratio) and subjective assessment that is placed the focus on edge characteristic for assessment of result in computer simulation. The results show that the proposed adaptive method is better than the conventional methods.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.1-5
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• 2007

In this paper, we propose an unmanned patient monitoring system design and performance of a motion capture and sound detection. Unmanned patient monitoring system can be used in the greek koma and meaning deep sleep patient to need 24 hour surveillance. To monitoring, we used laptop, CCTV camera (or PC camera), A/D converter, microphone and detection program. The detection program based on the frame difference method and sound level meter. It had several functions such as data collecting and storing. All of this system was tested in several the simulations of emergency situations. It can be expected that an unmanned patient monitoring system can be used in emergency situation and patient care.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.536-538
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• 2005

As multimedia data and huge-Quantity video data having been increasingly and commonly used, the key frame algorithm, as one of the methods for manipulating these kinds of data, became an important matter and has been studied for many years. But the formerly proposed key frame extraction methods take much processing time or need complex calculations due to decoding processes. In order to solve these problems which the former methods have and to enhance the key frame extraction efficiency, a novel key frame extraction method in compressed domain is proposed in this paper. In this method we get an edge histogram for each I-frame in DCT domain and then extract the key frames by means of histogram difference metric. Experimental results show that our algorithm achieves fast processing speed and high accuracy.