• Title/Summary/Keyword: Background Model

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Real-time 3D model generation system using multi-view images (다시점 영상을 이용한 실시간 3D 모델 생성 시스템)

  • Park, Jeong-Sun;Son, Hyung-Jae;Park, Jeung-Chul;Oh, Il-Seok
    • Journal of Digital Contents Society
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    • v.18 no.2
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    • pp.383-392
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    • 2017
  • This paper introduces a real-time 3D model generation system that can process in real time from multi-view image acquisition to image-based 3D model generation. This system describes how to collect, transmit, and manage the HD images input from 18 cameras and explain the background separation and smooth 3D volume model generation process. This paper proposes a new distributed data transmission and reception method for real-time processing of HD images input from 18 cameras. In addition, we describe a codebook-based background separating algorithm and a modified marching cube algorithm using perspective difference interpolation to generate smooth 3D models from multi-view images. The system is currently being built with a throughput rate of 30 frames per second.

An Efficient Background Modeling and Correction Method for EDXRF Spectra (EDXRF 스펙트럼을 위한 효율적인 배경 모델링과 보정 방법)

  • Park, Dong Sun;Jagadeesan, Sukanya;Jin, Moonyong;Yoon, Sook
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.8
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    • pp.238-244
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    • 2013
  • In energy dispersive X-ray fluorescence analysis, the removal of the continuum on which the X-ray spectrum is superimposed is one of the most important processes, since it has a strong influence on the analysis result. The existing methods which have been used for it usually require tight constraints or prior information on the continuum. In this paper, an efficient background correction method is proposed for Energy Dispersive X-ray fluorescence (EDXRF) spectra. The proposed method has two steps of background modeling and background correction. It is based on the basic concept which differentiates background areas from the peak areas in a spectrum and the SNIP algorithm, one of the popular methods for background removal, is used to enhance the performance. After detecting some points which belong to the background from a spectrum, its background is modeled by a curve fitting method based on them. And then the obtained background model is subtracted from the raw spectrum. The method has been shown to give better results than some of traditional methods, while working under relatively weak constraints or prior information.

Background Generation using Temporal and Spatial Information of Pixels (시간축과 공간축 화소 정보를 이용한 배경 생성)

  • Cho, Sang-Hyun;Kang, Hang-Bong
    • The KIPS Transactions:PartB
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    • v.17B no.1
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    • pp.15-22
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    • 2010
  • Background generation is very important for accurate object tracking in video surveillance systems. Traditional background generation techniques have some problems with non-moving objects for longer periods. To overcome this problem, we propose a newbackground generation method using mean-shift and Fast Marching Method (FMM) to use pixel information along temporal and spatial dimensions. The mode of pixel value density along time axis is estimated by mean-shift algorithm and spatial information is evaluated by FMM, and then they are used together to generate a desirable background in the existence of non-moving objects during longer period. Experimental results show that our proposed method is more efficient than the traditional method.

SFMOG : Super Fast MOG Based Background Subtraction Algorithm (SFMOG : 초고속 MOG 기반 배경 제거 알고리즘)

  • Song, Seok-bin;Kim, Jin-Heon
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1415-1422
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    • 2019
  • Background subtraction is the major task of computer vision and image processing to detect changes in video. The best performing background subtraction is computationally expensive that cannot be used in real time in a typical computing environment. The proposed algorithm improves the background subtraction algorithm of the widely used MOG with the image resizing algorithm. The proposed image resizing algorithm is designed to drastically reduce the amount of computation and to utilize local information, which is robust against noise such as camera movement. Experimental results of the proposed algorithm have a classification capability that is close to the state of the art background subtraction method and the processing speed is more than 10 times faster.

Detecting Foreground Objects Under Sudden Illumination Change Using Double Background Models (이중 배경 모델을 이용한 급격한 조명 변화에서의 전경 객체 검출)

  • Saeed, Mahmoudpour;Kim, Manbae
    • Journal of Broadcast Engineering
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    • v.21 no.2
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    • pp.268-271
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    • 2016
  • In video sequences, foreground object detection being composed of a background model and a background subtraction is an important part of diverse computer vision applications. However, object detection might fail in sudden illumination changes. In this letter, an illumination-robust background detection is proposed to address this problem. The method can provide quick adaption to current illumination condition using two background models with different adaption rates. Since the proposed method is a non-parametric approach, experimental results show that the proposed algorithm outperforms several state-of-art non-parametric approaches and provides low computational cost.

A Shadow Region Suppression Method using Intensity Projection and Converting Energy to Improve the Performance of Probabilistic Background Subtraction (확률기반 배경제거 기법의 향상을 위한 밝기 사영 및 변환에너지 기반 그림자 영역 제거 방법)

  • Hwang, Soon-Min;Kang, Dong-Joong
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.1
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    • pp.69-76
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    • 2010
  • The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

People Detection Algorithm in the Beach (해변에서의 사람 검출 알고리즘)

  • Choi, Yu Jung;Kim, Yoon
    • Journal of Korea Multimedia Society
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    • v.21 no.5
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    • pp.558-570
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    • 2018
  • Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

Study on the Periodic Flows in a Rectangular Container Under a Background Rotation

  • Suh, Yong-Kweon;Park, Jae-Hyun;Kim, Sung-Kyun;Son, Young-Rak
    • Journal of Mechanical Science and Technology
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    • v.18 no.4
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    • pp.671-680
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    • 2004
  • We present numerical and experimental results of the periodic flows inside a rectangular container under a background rotation. In numerical computation, a parallel-computation technique with MPI is implemented. Flow visualization and PIV measurement are also performed to obtain velocity fields at the free surface. Through a series of numerical and experimental works, we aim to clarify the fundamental reasons of discrepancy between the two-dimensional computation and the experimental measurement, which was detected in the previous study for the same flow model. Specifically, we check if the various assumptions prerequisite for the validity of the classical Ekman pumping law are satisfied for periodic flows under a background rotation.

A Study on the Power Spectral Analysis of Background EEG with Pisarenko Harmonic Decomposition (Pisarenko Harmonic Decomposition에 의한 배경 뇌파 파워 스펙트럼 분석에 관한 연구)

  • Jung, Myung-Jin;Hwang, Soo-Young;Choi, Kap-Seok
    • Proceedings of the KIEE Conference
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    • 1987.07b
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    • pp.1271-1275
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    • 1987
  • With the stochastic process which consists of the harmonic sinusoid and the white nosie, the power spectrum of background EEG is estimated by the Pisarenko Harmonic Decomposition. The estimating results are examined and compared with the results from the maximum entropy spectral estimation, and the optimal order of this model can be determined from the eigen value's fluctuation of autocorrelation of background EEG. From the comparing results, this paper ensures that this method is possible to analyze the power spectrum of background EEG.

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Study on the Periodic Flows in a Rectangular Container under a Background Rotation (직사각형 용기내의 주기유동에 관한 연구)

  • Park Jae Hyun;Suh Yong Kweon
    • Proceedings of the KSME Conference
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    • 2002.08a
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    • pp.325-328
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    • 2002
  • We present numerical and experimental result of the rotating flows inside a rectangular container under a background rotation. In numerical computation, a parallel-computation technique with MPI is implemented. Flow visualization and PIV measurement are also performed to obtain velocity fields at the free surface. Through a series of numerical and experimental works, we aim to clarify, if any, the fundamental reasons of discrepancy between the two-dimensional computation and the experimental measurement, which was detected in the previous study for the same flow model. Specifically, we check if the various assumptions prerequisite for the validity of the classical Ekman pumping law are satisfied for periodic flows under a background rotation.

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