In this paper, we propose an algorithm to automatically detect gait's joints. The proposed method classifies gait's types into front gait and flank gait so as to automatically detect gait's joints. And then according to classified types, the proposed applies joint extracting algorithm to input images. Firstly, we split input images into foreground image using difference images of Hue and gray-scale image of input and background one and extract gait's object. The proposed method classifies gaits into front gait and flank gait using ratio of Face's width to torso's width. Then classified gait's type, joints are detected 10 at front gait and detected 7~8 at flank gait. The proposed method is applied to the camera's input and the result shows that the proposed method automatically extracts joints.

We proposed a fast detection of copy-move image forgery using four step search algorithm in the spatial domain. In the four-step search algorithm, the search area is 21 (-10 ~ +10), and the number of pixels to be scanned is 33. Our algorithm reduced computational complexity more than conventional copy move image forgery methods. The proposed method reduced 92.34 % of computational complexity compare to exhaustive search algorithm.

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.

The mobile characteristics of the smart phone are well suited to on-the-site oriented services and are being utilized in a variety of ways. However, since most applications operate on a central server basis, Internet access is inevitable, and there are various problems such as an increase in server maintenance cost and security deterioration. Although researches have been proposed to utilize the smart phone itself as a server to improve it, these methods are based on downsizing the PC-based web server or modifying the smart phone kernel. These methods have the problems that a end user has to use the rooting, and they are not able to effectively utilize various communication means of a smart phone. In this paper, we propose a design and implementation of an application level smart phone web server. The server receives the document request from the remote via the Internet and in the local area using the WPAN. At this time, the web document is stored as an internal file structure of the smart phone application. We proposed the phttp protocol which uses server's phone number as ID for document request. Through the implementation, we analyzed the capacity and latency of stored data and its effectiveness is evaluated.

Nowadays a personalization algorithm is gaining huge attention. It gives users selective information which is helpful and interesting in a deluge of information based on their past behavior on the internet. However there is also a fatal side effect that the user can only get restricted information on restricted topics selected by the algorithm. Basically, the personalization algorithm makes users have a narrower perspective and even stronger bias because users have less chances to get views of opponent. Eli Pariser called this problem the 'filter bubble' in his book. It is important to understand exactly what a filter bubble is to solve the problem. Therefore, this paper shows how much Google's personalized search algorithm influences search result through an experiment with deep neural networks acting like users. At the beginning of the experiment, two Google accounts are newly created, not to be influenced by the Google's personalized search algorithm. Then the two pure accounts get politically biased by two methods. We periodically calculate the numerical score depending on the character of links and it shows how biased the account is. In conclusion, this paper shows the formation process of filter bubble by a personalization algorithm through the experiment.

A major accident occurring on the bed is falls that occur during at times when the care of nurses or protectors is inadequate, which is fatal to patients or the elderly. In particular, Enuresis or sleepiness caused by sleep apnea increases the risk of falls. Therefore, it is very important to detect falls and sleep apnea of patients without infringing privacy in the bed to patient's safety and accident prevention. In this paper, we reviewed the technologies developed for bed monitoring and implemented a non-intrusive monitoring system. The Occupancy Sensor allows the temperature of the bed and surrounding area to be extracted to enable track of the patient's motion. The Doppler Radar detects the patient's movements at normal times and the respiration state when patients have no movement during sleeping. It is specially designed for real-time monitoring of falling and respiration during sleeping through contactless multi-sensing while solving patient's privacy problems.

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.

It is required for today's video contents to interact with a viewer in order to provide more personalized experience to viewer(s) than before. In order to do so by providing friendly experience to a viewer from video contents' systemic perspective, understanding and analyzing the situation of the viewer have to be preferentially considered. For this purpose, it is effective to analyze the situation of a viewer by understanding the state of the viewer based on the viewer' s behavior(s) in the process of watching the video contents, and classifying the behavior(s) into the viewer's emotion and state during the flow. The term 'Flow-emotion-state' presented in this study is the state of the viewer to be assumed based on the emotions that occur subsequently in relation to the target video content in a situation which the viewer of the video content is already engaged in the viewing behavior. This Flow-emotion-state of a viewer can be expected to be utilized to identify characteristics of the viewer's Flow-situation by observing and analyzing the gesture and the facial expression that serve as the input modality of the viewer to the video content.

Advances of telecommunication technology bring various changes in journalism field. Reporters started to gather, edit, and transmit content to main server in media company using hand-held smart media and notebook computer. This paper tried to testify valid bit-rate of visual news content using LTE network and mobile phone. Field news like natural disasters need real-time transmission of video content. But broadcasting company normally use heavy ENG system and transmission satellite trucks. We prepared and experimented different types of visual content that has different bit-rates. Transmission tool was LU-60HD mobile system of LiveU Corporation. Transmission result shows that bit-rate of 2Mbps news content is not suitable for broadcasting and VBR (Variable Bit Rate) transmission has better definition quality than CBR (Constant Bit Rate) method. Three different bit-rate of VBR transmission result shows that 5Mbps clip has better quality than 1Mbps and 3Mbps. The higher bit-rate, the better video quality. But if the content has much movements, that cause delay and abnormal quality of video. So optimizing the balance between stability of signal and quality of bit-rate is crucial factor of real-time broadcasting news gathering business.

In this paper, we try to verify a normative module based house design procedure consisted of several sequential steps. The first step is to suggest formalization of designing so that we could clarify each phase and operation we are adopting in our design process. The second step is the clearing up the conceptual schema of traditional skills that we adopt and utilize from traditional Hanok framing techniques. The third step is to formulate adequate modular kits for the assembly of house design solutions for the schematic, conceptual and preliminary phases of designing. The fourth step is to implementing our ideas and methods to a proper computational platform such as Unity3D. The final step is to verify our symbolic descriptions of design formalization with the output of our experiments so that we have better understanding of design reasoning characteristics such as in house design.

In this study, we propose a marine character prototype study for development of a marine character as digital contents. This study is a precedent study to build a marine character database with the production of digital contents based on "Jasan-urbo". "Jasan-urbo" is a representative cultural heritage that can highlight the value of Korean marine culture as the first illustrated book of marine creatures in Korea. Therefore, we examined the use value of "Jasan-urbo" through the content approach and looked at the visualization for character utilization and then designed a marine fish species classification standard model. Finally, this study proposed the possibility of discovering prototype sources for digitalization of Korean marine creature's resources.