• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.322-333
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• 2019

After the launch of smart phones, various miniaturized smart devices such as wearable and IOT devices have deeply embedded in human life, and have created a technology-oriented society. In this technology-oriented society, technology development itself is important, however it seems more important to utilize existing technology appropriately and deliver effectively to human life. As the computer became personalized after the appearance of PC, human-centered computing such as HCI and UCD had begun to appear. However, most of the researches focused on technology that made human being convenient to interact with computer such as computer systems design and UX development. In the technology-oriented society, it seems more urgent to apply existing technology to human life. In this paper, we propose a methodology, 'Digital Leveraging' which guides how to effectively apply technology to human life. Digital Leveraging is the way of convergence between technology and humanities.

• 전자공학회논문지SC
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• 제42권4호
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• pp.11-18
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• 2005

인간은 의사 표현을 위해 음성언어 뿐 아니라 몸짓 언어(body languages)를 많이 사용한다 이 몸짓 언어 중 대표적인 것은, 물론 손과 팔의 사용이다. 따라서 인간 팔의 운동 해석은 인간과 기계의 상호 작용(human-computer interaction)에 있어 매우 중요하다고 할 수 있다. 이러한 견지에서 본 논문에서는 다음과 같은 방법으로 컴퓨터비전을 이용한 인간팔의 3차원 자세 추정 방법을 제안하다. 먼저 팔의 운동이 대부분 회전 관절(revolute-joint)에 의해 이루어진다는 점에 착안하여, 컴퓨터 비전 시스템을 활용한 회전 관절의 3차원 운동 해석 기법을 제안한다. 이를 위해 회전 관절의 기구학적 모델링 기법(kinematic modeling techniques)과 컴퓨터 비전의 경사 투영 모델(perspective projection model)을 결합한다. 다음으로, 회전 관절의 3차원 운동해석 기법을 컴퓨터 비전을 이용한 인간 팔의 3차원 자세 추정 문제에 웅용한다. 그 기본 발상은 회전 관절의 3차원 운동 복원 알고리즘을 인간 팔의 각 관절에 순서 데로 적용하는 것이다. 본 알고리즘은 특히 유비쿼터스 컴퓨팅(ubiquitous computing)과 가상현실(virtual reality)를 위한 인간-컴퓨터 상호작용(human-computer interaction)이라는 응용을 목표로, 고수준의 정확도를 갖는 폐쇄구조 형태(closed-form)의 해를 구하는데 주력한다.

• 한국국방경영분석학회지
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• 제9권2호
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• pp.45-55
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• 1983

Film data photographed by Motor Drive Camera were gathered and recorded in the FM Tape Recorder via computer-aided Location Analyzer and Voltage Generator. The recorded analogue data are converted into digital voltage values corresponding to the location of 14 landmarks by Analog-to-Digital Converter attached to digital computer. Using these converted values, the human motions were reproduced by CalComp Plotter and computer screen. This author concludes that any human motions can be analyzed by computer and we can find some methods of improvements of motions in work places, sports science, or operations of military equipments.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2002년도 추계학술발표논문집 (상)
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• pp.743-746
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• 2002

바이오 인포매틱스(bioinformatics) 분야 중 한 분야인 셀 기반 분석(cell-based assay) 시스템 구축의 필요성이 최근 대두되고 있다. 특정 시약 또는 시험 물질을 셀 세포에 투여했을 때 시간 축 변화에 따라 변화하는 세포의 변화를 감지하기 위해서 세포 영상의 영역 분할이 선행되어야 한다. 본 논문에서는 전체 영상에 대하여 셀 공통 영역을 추출하고, 추출된 공통영역을 스네이크(snake) 기법을 이용하여 세포 영역을 분할하는 방법을 제안하고자 한다.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1248-1257
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• 2017

We propose a frequency estimation algorithm of human movements using Kinect. We collect the 3D coordinates of the joints of a human body and then obtain the frequency-domain description of the movements using the discrete Fourier transform (DFT). By choosing the frequency with the biggest magnitude in the selected frequencies of each of human's joint, we obtain the major beat of the human movements. Experimental results show that the proposed algorithm accurately estimates the frequency of human movements. We expect that the proposed algorithm would be applied to many AR and VR applications as a preprocessing.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• 대한인간공학회지
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• 제12권1호
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• pp.57-73
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• 1993

In this paper, methods of human figure representation in computer graphics are described. Many applications of the human figure representation are found in areas including industry, advertisement, and cartoon production and further research for the methods that show the human figure more realistically is ex- pected. Two analytic methods for human model, kinematics and dynamics, are ex- plained and the characteristics of the man-machine interface systems that include human figure representation are presented. Various techniques of the human figure representation based on kinematics or(and) dynamics are discussed and representation methods of human body segments such as hand, face, spine are introduced in this paper.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.146-158
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• 2023

Smart City is gaining attention with the advancement of Information and Communication Technology (ICT). ICT provides the basis for smart city foundation; enables us to interconnect all the actors of a smart city by supporting the provision of seamless ubiquitous services and Internet of Things. On the other hand, Crowdsourcing has the ability to enable citizens to participate in social and economic development of the city and share their contribution and knowledge while increasing their socio-economic welfare. This paper proposed a hybrid model which is a compound of human computation, machine computation and citizen crowds. This proposed hybrid model uses knowledge-based crowdsourcing that captures collaborative and collective intelligence from the citizen crowds to form democratic knowledge space, which provision solutions in areas of civic innovations. This paper also proposed knowledge-based crowdsourcing framework which manages knowledge activities in the form of human computation tasks and eliminates the complexity of human computation task creation, execution, refinement, quality control and manage knowledge space. The knowledge activities in the form of human computation tasks provide support to existing crowdsourcing system to align their task execution order optimally.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권1호
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (B)
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• pp.509-514
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• 2006

Human action recognition is an active research area in computer vision. In this paper, we present a robust method for human action recognition by using combined information of human body shape and motion information with multiple views image sequence. The principal component analysis is used to extract the shape feature of human body and multiple block motion of the human body is used to extract the motion features of human. This combined information with multiple view sequences enhances the recognition of human action. We represent each action using a set of hidden Markov model and we model each action by multiple views. This characterizes the human action recognition from arbitrary view information. Several daily actions of elderly persons are modeled and tested by using this approach and they are correctly classified, which indicate the robustness of our method.