• Title/Summary/Keyword: 가우시안 분해

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AdaBoost-based Gesture Recognition Using Time Interval Window Applied Global and Local Feature Vectors with Mono Camera (모노 카메라 영상기반 시간 간격 윈도우를 이용한 광역 및 지역 특징 벡터 적용 AdaBoost기반 제스처 인식)

  • Hwang, Seung-Jun;Ko, Ha-Yoon;Baek, Joong-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.3
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    • pp.471-479
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    • 2018
  • Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

A Novel Method for Automated Honeycomb Segmentation in HRCT Using Pathology-specific Morphological Analysis (병리특이적 형태분석 기법을 이용한 HRCT 영상에서의 새로운 봉와양폐 자동 분할 방법)

  • Kim, Young Jae;Kim, Tae Yun;Lee, Seung Hyun;Kim, Kwang Gi;Kim, Jong Hyo
    • KIPS Transactions on Software and Data Engineering
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    • v.1 no.2
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    • pp.109-114
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    • 2012
  • Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

Multi-fidelity Data-fusion for Improving Strain accuracy using Optical Fiber Sensors (이종 광섬유 센서 데이터 융합을 통한 변형률 정확도 향상 기법)

  • Park, Young-Soo;Jin, Seung-Seop;Yoo, Chul-Hwan;Kim, Sungtae;Park, Young-Hwan
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.40 no.6
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    • pp.547-553
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    • 2020
  • As aging infrastructures increase along with time, the efficient maintenance becomes more significant and accurate responses from the sensors are pre-requisite. Among various responses, strain is commonly used to detect damage such as crack and fatigue. Optical fiber sensor is one of the promising sensing techniques to measure strains with high-durability, immunity for electrical noise, long transmission distance. Fiber Bragg Grating (FBG) is a point sensor to measure the strain based on reflected signals from the grating, while Brillouin Optic Correlation Domain Analysis (BOCDA) is a distributed sensor to measure the strain along with the optical fiber based on scattering signals. Although the FBG provides the signal with high accuracy and reproducibility, the number of sensing points is limited. On the other hand, the BOCDA can measure a quasi-continuous strain along with the optical fiber. However, the measured signals from BOCDA have low accuracy and reproducibility. This paper proposed a multi-fidelity data-fusion method based on Gaussian Process Regression to improve the fidelity of the strain distribution by fusing the advantages of both systems. The proposed method was evaluated by laboratory test. The result shows that the proposed method is promising to improve the fidelity of the strain.

Development of Adaptive Spatial Filter to Improve Noise Characteristics of PET Images (PET 영상의 잡음개선을 위한 적응적 공간 필터 개발)

  • Woo, S. K.;Choi, Y.;Im, K. C.;Song, T. Y.;Jung, J. H.;Lee, K. H.;Kim, S. E.;Choe, Y. S.;Park, C. C.;Kim, B. T.
    • Journal of Biomedical Engineering Research
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    • v.23 no.3
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    • pp.253-261
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    • 2002
  • A spatially adaptive falter was formulated to imrove PET image qualify and the Performance of the filter was evaluated using simulation and phantom and human PET studies. In the proposed filter. if a pixel was identified as the edge Pixel, the Pixel value was Preserved. Otherwise a Pixel was replaced by the mean of the pixel values weighted by 2:7: 2. A Pixel was identified as the edge Pixel. if it satisfies the following conditions : the number of ADs (absolute difference between center and neighborhood pixels) which is smaller than THl (($pix_max{\times}0.1/log_2(NPM)$, NPM : mean of 6 neighborhood pixels excluding minimum and maximum) is 8-k and the number of ADs which is lager than TH2 ($NPM{\times}0.1$) is k. where k : 2, 3, …, 6. The results of this study demonstrate the superior performance of the Proposed titter compared to Gaussian fitter, weight median filter and subset averaged median filter. The proposed tittering method is simple but effective in increasing uniformity and contrast with minimal degradation of spatial resolution of PET images and thus. is expected to Provide improved diagnositc quality PET images .