• Title/Summary/Keyword: Spatiotemporal

검색결과 603건 처리시간 0.03초

동영상을 위한 적응 방향성 필터링 기술 (Adaptive Directional Filtering Techniques for Image Sequences)

  • 고성제
    • 한국통신학회논문지
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    • 제18권7호
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    • pp.922-934
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    • 1993
  • 본 논문에서는, 동영상 처리에 효과적으로 사용되고 있는 시공간 중간 가중 미디안(spatiotemporal center weighted median, CWM) 필터의 통계적 특성을 고찰한 결과, 중간 가중 미디안 필터는 잡음 감쇄 효과를 회생시킴으로써 동영상의 구조들을 보존할 수 있다는 것을 보였다. 또한 동영상에서, 보다 효과적으로 이용될 수 있는 적응 방향성 중간 가중 미디안(adaptive directional center weighted median, ADCWM) 필터를 제안하였다. 제안된 이 필터는 매 윈도우내에서 중심의 양쪽에 대칭인 한쌍의 oreder statistics를 국소 영상의 통계치에 의해 선택하는 적응 대칭성 order statistics(ASOS) 연산자에 기반을 두고 있으며 또한 다단 필터링 구조를 채택하고 있다. 적응 방향성 중간 가중 미디안 필터는 움직임 추정(motion estimation) 기술을 이용하지 않고 잡음을 줄이며 또한 동영상의 구조를 보존할 수 있다는 것을 실험을 통하여 입증하였다.

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Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

  • Kamal, Shaharyar;Jalal, Ahmad;Kim, Daijin
    • Journal of Electrical Engineering and Technology
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    • 제11권6호
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    • pp.1857-1862
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    • 2016
  • Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

Human Activity Recognition Using Spatiotemporal 3-D Body Joint Features with Hidden Markov Models

  • Uddin, Md. Zia;Kim, Jaehyoun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권6호
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    • pp.2767-2780
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    • 2016
  • Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

SDE 공간 모델의 이력지원 확장 (A historical Extension for SDE Data Model)

  • 이종연;안병익;류근호
    • 한국정보처리학회논문지
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    • 제5권9호
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    • pp.2281-2293
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    • 1998
  • 공간상의 지형객체는 토지의 소유주 변경이나 소유주의 신상 변경, 토지의 합병과 분할, 도로의 신설 및 변경에 따른 지형 변경 등과 같은 공간 또는 속성 변화에 의해 공간이력 데이터가 발생한다. 하지만 기존의 GIS(Geographic Information System)는 측정시점에서 지형객체의 스냅샷 정보만을 관리하므로 시간 흐름에 따른 공간이력을 유지할 수 없다. 따라서 이 연구는 공간 데이터베이스의 시간차원 확장을 통하여 시간과 공간을 동시에 지원하는 시공간 데이터베이스 모델과 이력 관리 체계를 설계하고 구현한다. 이 연구의 결과는 GIS 공간 데이터베이스의 시간차원 확장으로 지형객체의 모든 이력정보를 유지 관리할 수 있다.

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Virtual Reality Community Gait Training Using a 360° Image Improves Gait Ability in Chronic Stroke Patients

  • Kim, Myung-Joon
    • The Journal of Korean Physical Therapy
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    • 제32권3호
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    • pp.185-190
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    • 2020
  • Purpose: Gait and cognitive impairment in stroke patients exacerbate fall risk and mobility difficulties during multi-task walking. Virtual reality can provide interesting and challenging training in a community setting. This study evaluated the effect of community-based virtual reality gait training (VRGT) using a 360-degree image on the gait ability of chronic stroke patients. Methods: Forty-five chronic stroke patients who were admitted to a rehabilitation hospital participated in this study. Patients meeting the selection criteria were randomly divided into a VRGT group (n=23) and a control group (n=22). Both these groups received general rehabilitation. The VRGT group was evaluated using a 360-degree image that was recorded for 50 minutes a day, 5 days per week for a total of 6 weeks after their training. The control group received general treadmill training for the same amount of time as that of the VRGT group. The improvement in the spatiotemporal parameters of gait was evaluated using a gait analyzer system before and after training. Results: The spatiotemporal gait parameters showed significant improvements in both groups compare with the baseline measurements (p<0.05), and the VRGT group showed more improvement than the control group (p<0.05). Conclusion: Community-based VRGT has been shown to improve the walking ability of chronic stroke patients and is expected to be used in rehabilitation of stroke patients in the future.

도시성장에 따른 토지이용패턴의 시공간적 영향 평가 (The Spatiotemporal Impact of Urban Growth based on Landuse Pattern)

  • 이동근;최혜영;오규식
    • 환경영향평가
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    • 제18권3호
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    • pp.161-170
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    • 2009
  • As urban growth continues, the earth ecosystem is increasingly dependent on the patterns of urban growth. The impact intensity from urban growth is expected to change predictably with distance from the urban center. However we can't fully understand yet how urban development pattern affects urban ecosystem. In researches about urban ecosystem, it is important to relate the spatial pattern of urbanization to ecological processes. So we used gradient analysis with time data; 1980's, 1990's and 2000's. We attempted to quantify the urban spatiotemporal impacts in Daejeon-city and Cheonan-city, Korea, along a 75km long and 3km wide transect. Through the results, we found the impacts range of urbanization with urban development process of two cities. When the urban growth was concentrated on in both cities, the impacts intensity and range were much stronger and wider. As a result, in urban planning or green space planning, we have to consider suitable urban development forms with surrounding areas, and make legal clauses which limits landuse change. This quantifying the urban gradient is an important step in understanding urban ecology.

Spatiotemporal variations and source apportionment of NOx, SO2, and O3 emissions around heavily industrial locality

  • Al-Harbi, Meshari;Al-majed, Abdulrahman;Abahussain, Asma
    • Environmental Engineering Research
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    • 제25권2호
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    • pp.147-162
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    • 2020
  • The main objective of this study is to estimate the levels of pollution to which the community is presently exposed and to model the regimes of local air quality. Diurnal, daily, and monthly variations of NO, NO2, SO2, and O3 were thoroughly investigated in three areas; namely, residential, industrial, and terminal in Ras Al-Khafji. There is obvious diurnal variation in the concentration of these pollutants that clearly follows the diurnal variation of atmospheric temperature and main anthropogenic and industrial activities. Correlation analysis showed that meteorological conditions play a vital role in shaping the pattern and transportation of air pollutants and photochemical processes affecting O3 formation and destruction. Bivariate polar plots, an effective graphical tool that utilizes air pollutant concentrations' dependence on wind speed and wind direction, were used to identify prevailing emission sources. Non-buoyant ground-level sources like domestic heating and street transport emissions, various industrial stacks, and airport-related activities were considered dominant emission sources in observatory sites. This study offers valuable and detailed information on the status of air quality, which has considerable, quantifiable, and important public health benefits.

디지털미디어를 이용한 디자인에 있어서 시공간 구조의 문제 (Problems of Spatiotemporal Structure in the Digital Design)

  • 김석화
    • 한국컴퓨터산업학회논문지
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    • 제5권1호
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    • pp.109-122
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    • 2004
  • 현대디자인의 변모에 가장 커다란 변화를 예고하고 전개하는 것이 바로 정보혁명에 기초한 멀티미디어의 등장이라 할 수 있다. 본 논문에서는 현금(現今)의 뉴미디어로서 디지털의 개념과 특성을 구체적으로 살펴보기 위해 변화된 환경에서 새로운 디자인 모델의 창출방법을 비교 분석하고 정보양식에 근거한 커뮤니케이션의 변화를 조망하고자 하였으며 디지털과 아날로그 속성의 비교분석과 디지털 개념과 특성을 구체화하기 위하여 디지털 미디어의 가장 커다란 특성이길 할 수 있는 ‘가상성(virtual reality)’과 ‘상호작용성(interaction)’에 대한 이론적 근거를 도출하였다. 따라서 본 논문에서는 위에서 언급된 문제점을 바탕으로 디지털디자인에서 나타나는 시공간구조의 새로운 해석방법으로 본 연구의 타당성을 입증하고자 하였다.

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Motion Estimation-based Human Fall Detection for Visual Surveillance

  • Kim, Heegwang;Park, Jinho;Park, Hasil;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • 제5권5호
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    • pp.327-330
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    • 2016
  • Currently, the world's elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera's optical axis.

An Optimized PI Controller Design for Three Phase PFC Converters Based on Multi-Objective Chaotic Particle Swarm Optimization

  • Guo, Xin;Ren, Hai-Peng;Liu, Ding
    • Journal of Power Electronics
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    • 제16권2호
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    • pp.610-620
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    • 2016
  • The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.