• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.150-157
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• 2007

The image calls forth the curiosity of the children. children experience the many thing through the image and it makes children to study naturally. Specially, the animation socializes children and it plays an important role in form of a self-identity. The gender-identity reappearance of the animation character is important. Gender-identity reappearance type of the animation character is able to classify with the reappearance type of visual information and the narrative. The narrative reappearance type does more clearly visual information reappearance type. Also classifying the gender-identity of animation character aspects into each type, they are; gender stereotype that character reflects the conservative and commercial ideology; gender non-stereotype that character doesn't reflect gender stereotype. But reappearance of the gender non-stereotype character as well, it is stopping in the exaggeration or the caricature of gender role of the character. Consequently the animation characters must be reappeared with the character which have the future oriented gender identity-as one human being that forms a value subjectively. The animation which reappears the future oriented gender-identity plants a proper self-perception and gender role in children and relaxes the traditional gender-identity which is already acquired.

• Journal of Platform Technology
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• v.6 no.2
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• pp.3-9
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• 2018

Recently VR environment is used in many areas including mobile learning, smart factory. However HMD(head-mounted display) is required to a dedicated and expensive system with high-end specification. When designing a VR system, it is needed to handle performance, mobility and usability. Many VR applications need to handle diverse sensors and user inputs continuously in a streaming manner. In this paper we design a VR mobile platform and implement a low-cost mobile VR HMD running on the platform. The VR HMD supports 3D contents delivery in a mobile manner. It is used to detect the motion detection based on angle value of a VR player from accelerator and gyro sensor. The MPU-6050, 6-axis sensor, is used to get a sensory value and the sensory value is taken as an input to a VR rendering server on a Unity game engine that is generated 3D images.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.55-64
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• 2010

This paper proposes a face detection method using Free Rectangle feature which possesses a quick execution time and a high efficiency. The proposed mask of Free Rectangle feature is composed of two separable rectangles with the same area. In order to increase the feature diversity, Haar-like feature generally uses a complex mask composed of two or more rectangles. But the proposed feature mask can get a lot of very efficient features according to any position and scale of two rectangles on the feature window. Moreover, the Free Rectangle feature can largely reduce the execution time since it is defined as the only difference of the sum of pixels of two rectangles irrespective of the mask type. Since it yields a quick detection speed and good detection rates on real world images, the proposed face detection method based on Adaboost algorithm is easily applied to detect another object by changing the training dataset.

• Korean Journal of Food Science and Technology
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• v.27 no.4
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• pp.525-531
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• 1995

Three quality factors of bread during baking process were measured to develop neural network models for bread baking process. Firstly, volume and browning changes during bread baking process were measured using image processing technique and temperature changes inside the bread during process were measured by K-type thermocouples. Relationships among them showed nonlinearity. Secondly, multilayer perception structure with error back propagation learning was used to construct neural network models. Three neural network models for volume, browning, and bread temperature were developed respectively. Developed models showed good performance with predictive error of 4.62% for volume and browning changes after 30 seconds, 7.38% for volume and browning changes after 2 minutes, and 1.09% for temperature change inside the bread respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.322-327
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• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.401-408
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• 1997

Human gets almist all of his knoweledge from the recognition and the accumulation of input patterns,image or sound,the he gets theough his eyes and through his ears.Among these means,his chracter recognition,an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning.Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so drrdtive until now.This stusy suggests a new type of recurrent neural network for an deedctive classification of the static patterns such as off-line handwritten chracters.Using the new J-E(Jordan-Elman)neural network model that enlarges and combines Jordan Model and Elman Model,this new type is better than those of before in recobnizing the static patterms such as figures and handwritten-characters.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.279-289
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• 2011

The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.

• Spatial Information Research
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• v.18 no.2
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• pp.13-24
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• 2010

The purpose of this research is to explore the possibility of geographic education by implementing the map-based geographic education site which mashed up with Google earth by referring the various materials of geographic education which exist in on-line and off-line. In recent years map-based geographic education is required by the radical change of geoweb environments, but there have been few researches in this field. This research is folded up as follows: First, we designed the contents through the textbook analysis and then collect various data related to the contents such as pictures, video clips, conceptual map, etc. which are required to explain the concept. Second, we mashed up the collected data on the Google earth by using the Google's open API. Third, we implemented the geographic education website based on the classification of contents in textbook and the various collected data. This research is important in both that it explores the possibility of the map-based education rather than the text-based education in the geographic field which handles mainly the space and finds the best method to express the various concepts of the textbook on the geoweb environments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.1-8
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• 2009

Detecting entry and exit zones in a view covered by multiple cameras is an essential step to determine the topology of the camera setup, which is critical for achieving and sustaining the accuracy and efficiency of multi-camera surveillance system. In this paper, a graph theoretic clustering method is proposed to detect zones using data points which correspond to entry and exit events of objects in the camera view. The minimum spanning tree (MST) is constructed by associating the data points. Then a set of well-formed clusters is sought by removing inconsistent edges of the MST, based on the concepts of the cluster balance and the cluster density defined in the paper. Experimental results suggest that the proposed method is effective, even for sparsely elongated clusters which could be problematic for expectation-maximization (EM). In addition, comparing to the EM-based approaches, the number of data required to obtain stable outcome is relatively small, hence shorter learning period.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.1-9
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• 2010

Generally, dynamic hand gesture recognition is formed through preprocessing step, hand tracking step and hand shape detection step. In this paper, we present advanced dynamic hand gesture recognizing method that improves performance in preprocessing step and hand shape detection step. In preprocessing step, we remove noise fast by using dynamic table and detect skin color exactly on complex background for controling skin color range in skin color detection method using YCbCr color space. Especially, we increase recognizing speed in hand shape detection step through detecting Start Image and Stop Image, that are elements of dynamic hand gesture recognizing, using Guideline. Guideline is edge of input hand image and hand shape for comparing. We perform various experiments with nine web-cam video clips that are separated to complex background and simple background for dynamic hand gesture recognition method in the paper. The result of experiment shows similar recognition ratio but high recognition speed, low cpu usage, low memory usage than recognition method using learning exercise.