• Title/Summary/Keyword: Ellipsoidal gate

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Motility Analysis of Gate Myocardium SPECT Image Using Left Ventricle Myocardium Model (좌심실 심근 모델을 이용한 게이트 심근 SPECT 영상의 운동성 분석)

  • 손병환;김재영;이병일;이동수;최흥국
    • Journal of Korea Multimedia Society
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    • v.6 no.3
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    • pp.444-454
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    • 2003
  • An analysis of heart movement is to estimate a role which supplies blood in human body. We have constructed a left ventricle myocardium model and mathematically evaluated the motion of myocardium. The myocardial motility was visualized using some parameters about cardiac motion. We applied the myocardium model in the gated myocardium SPECT image that showed a cardiac biochemical reaction, and analyzed a motility between the gated myocardium SPECT image and the myocardium model. The myocardium model was created of the based on three dimensional super-ellipsoidal model that was using the sinusoidal function. To express a similar form and motion of the left ventricle myocardium, we calculated parameter functions that gave the changing of motion and form. The LSF algorithm was applied to the myocardium gated SPECT image data and the myocardium model, and finally created a fitting model. Then we analyzed a regional motility direction and size of the gated myocardium SPECT image that was constructed on a fitting model. Furthermore, we implemented the Bull's Eye map that had evaluated the heart function for presentation of regional motility. Using myocardium's motion the evaluation of cardiac function of SPECT was estimated by a contraction ability, perfusion etc. However, it is not any estimation about motility. So, We analyzed the myocardium SPECT's motility of utilizing the myocardium model. We expect that the proposed algorithm should be a useful guideline in the heart functional estimation.

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Forward Mapping of Spaceborne SAR Image Coordinates to Earth Surface

  • Shin, Dong-Seok;Park, Won-Kyu
    • Korean Journal of Remote Sensing
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    • v.18 no.5
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    • pp.273-280
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    • 2002
  • This paper describes a mathematical model and its utilization algorithm for calculating the accurate target position on the ellipsoidal earth surface which corresponds to a range-azimuth coordinates of unprocessed synthetic aperture radar (SAR) images. A geometrical model which is a set of coordinate transformations is described. The side-looking directional angle (off-nadir angle) is determined in an iterative fashion by using the model and the accurate slant range which is calculated from the range sampling timing of the instrument. The algorithm can be applied not only for the geolocation of SAR images but also for the high quality SAR image generation by calculating accurate Doppler parameters.

A real-time multiple vehicle tracking method for traffic congestion identification

  • Zhang, Xiaoyu;Hu, Shiqiang;Zhang, Huanlong;Hu, Xing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.6
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    • pp.2483-2503
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    • 2016
  • Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.