• Journal of Korea Multimedia Society
• /
• v.22 no.2
• /
• pp.322-333
• /
• 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.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.4 s.304
• /
• pp.11-18
• /
• 2005

For expressing intention the human often use body languages as well as vocal languages. Of course the gestures using arms and hands are the representative ones among the body languages. Therefore it is very important to understand the human arm motion in human-computer interaction. In this respect we present here how to estimate 3D pose of human arms by using computer vision systems. For this we first focus on the idea that the human arm motion consists of mostly revolute joint motions, and then we present an algorithm for understanding 3D motion of a revolute joint using vision systems. Next we apply it to estimating 3D pose of human arms using vision systems. The fundamental idea for this algorithm extension is that we may apply the algorithm for a revolute joint to each of the revolute joints of hmm arms one after another. In designing the algorithms we focus on seeking closed-form solutions with high accuracy because we aim at applying them to human computer interaction for ubiquitous computing and virtual reality.

• Journal of the military operations research society of Korea
• /
• v.9 no.2
• /
• pp.45-55
• /
• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11a
• /
• pp.743-746
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.20 no.8
• /
• pp.1248-1257
• /
• 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
• /
• v.10 no.4
• /
• pp.1910-1914
• /
• 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.

• Journal of the Ergonomics Society of Korea
• /
• v.12 no.1
• /
• pp.57-73
• /
• 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
• /
• v.23 no.8
• /
• pp.146-158
• /
• 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)
• /
• v.3 no.1
• /
• pp.84-95
• /
• 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%.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.509-514
• /
• 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.