• Journal of the Korean Society of Radiology
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• v.14 no.5
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• pp.585-595
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• 2020

Magnetic resonance imaging(MRI) has become an important technique for examining changes in human brain structure with neurological disorders. Brain development is a very complex process, and is affected by neurogenesiss and genetic programs. As age increases, structures of the brain change, which can contribute to the formation of brain diseases. Among the various factors, Gender is one of the greatest influential factors that affect the development of a healthy brain. The images were analyzed through various programs found in FSL such as SIENAX, FIRST, and Vertex analysis. Our results show that significant gender-related differences in subcortical areas were observed at the particular age group. The magnitude of these differences between gender and volume varied depending on the area investigated. In this study, we used more advanced 3T MRI for the structural analysis of subcortical structures between each gender. In addition, Vertex Analysis was used to visualize the volumetric differences in subcortical structures between each gender. This study is limited to groups in their 70s, therefore, further studies are needed for wider age groups.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Cartoon and Animation Studies
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• s.45
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• pp.27-50
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• 2016

The present study argues that documentary-animation films, which are based on actual human voices, on the level of representation, constitute a new expansion for the medium of animation films, which serve as testimonies to the real world. Animation films are produced using very diverse techniques so that they are complex to the degree of being indefinable, and documentary films, though based on objective representation, increase in complexity in that there exist various types of artificial interventions such as direction and digital image processing. Having emerged as a hybrid genre of the two media, documentary-animation films draw into themselves actual events and elements so that they conceptually share reality-based narratives and are visually characterized by the trappings of animation films. Generally classified as 'animated documentaries', this genre triggered discussions following the release of , a work that is mistaken as having used rotoscoping transforming live action in terms of the technique. When analyzed in detail, however, this work is presented as an ambiguous medium where the characteristics of animation films, which are virtual simulacra without reality, and of documentaries, which are based on the objective indexicality of the referents, coexist because of its mixed use of typical animation techniques, 3D programs, and live-action images. Discussed in the present study, , , and share the characteristics of the medium of documentaries in that the narratives develop as testimonies of historical figures but, at the same time, are connected to animation films because of their production techniques and direction characteristics. Consequently, this medium must be discussed as a new expansion rather than being included in the existing classification system, and such a presupposition is an indispensable process for directly facing the reality of the works and for developing discussions. Through works that directly use the interviewees' voices yet do not transcend the characteristics of animation films, the present study seeks to define documentary-animation films and to discuss the possibility of the medium, which has expanded as a testimony to the real world.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.679-691
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• 2002

Volume visualization is an important subarea of scientific visualization, and is concerned with techniques that are effectively used in generating meaningful and visual information from abstract and complex volume datasets, defined in three- or higher-dimensional space. It has been increasingly important in various fields including meteorology, medical science, and computational fluid dynamics, and so on. On the other hand, virtual reality is a research field focusing on various techniques that aid gaining experiences in virtual worlds with visual, auditory and tactile senses. In this paper, we have developed a visualization system for CAVE, an immersive 3D virtual environment system, which generates stereoscopic images from huge human volume datasets in real-time using an improved volume visualization technique. In order to complement the 3D texture-mapping based volume rendering methods, that easily slow down as data sizes increase, our system utilizes an image-based rendering technique to guarantee real-time performance. The system has been designed to offer a variety of user interface functionality for effective visualization. In this article, we present detailed description on our real-time stereoscopic visualization system, and show how the Visible Korean Human dataset is effectively visualized on CAVE.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.474-486
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• 2010

In this paper, we propose a new method for detection moving object contour using spatial and temporal edge. In general, contour pixels of the moving object are likely present around pixels with high gradient value along the time axis and the spatial axis. Therefore, we can detect the contour of the moving objects by finding pixels which have high gradient value in the time axis and spatial axis. In this paper, we introduce a new computation method, termed as temporal edge, to compute an gradient value along the time axis for any pixel on an image. The temporal edge can be computed using two input gray images at time t and t-2 using the Sobel operator. Temporal edge is utilized to detect a candidate region of the moving object contour and then the detected candidate region is used to extract spatial edge information. The final contour of the moving object is detected using the combination of these two edge information, which are temporal edge and spatial edge, and then the post processing such as a morphological operation and a background edge removing procedure are applied to remove noise regions. The complexity of the proposed method is very low because it dose not use any background scene and high complex operation, therefore it can be applied to real-time applications. Experimental results show that the proposed method outperforms the conventional contour extraction methods in term of processing effort and a ghost effect which is occurred in the case of entropy method.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.753-763
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• 2018

The image of scientists that learners have has an important impact on science learning and on science-related career choices. The image of the scientist was mainly analyzed using the drawing analysis method. Drawing analysis has limitations on drawing, mainly analyzing the external image of scientist. Science teachers' images of scientists and their perception of science-related careers are important factors in students' science learning and science-related career choices. However, research on science teachers is lacking. Therefore, the purpose of this study is to investigate the usefulness of measurement tools by developing and applying a scientist image measurement tool through the semantic analysis method, and to discuss the educational implications of the research by investigating the image of scientists and science-related professions of secondary pre-service science teachers. The subjects of the study were 79 male and 55 female for a total of 134 students in the 2nd and 3rd grades majoring in science education at a teachers college. The results of the research show that the image measurement tool consisted of four components: 'ability,' 'evaluation,' 'activity,' and 'emotion,' in 24 items. As a result of applying the developed measurement tool to the secondary pre-service science teachers, the image of the 'evaluation,' 'ability,' and 'activity' elements of the scientist were high, but 'emotion' was low. There was no statistically significant difference according to gender. It is found that science-related career perceive them as 'hard,' 'professional,' 'smart,' and 'complex.' In particular, male students perceive themselves as 'hard and difficult' while female students perceive it as 'challenging and complicated'. Finally, we discussed the usefulness of using the image measurement tool of the scientists, the image of the scientists of the secondary pre-service science teachers, and the educational implications on science-related career.

• Journal of the Korean Society of Costume
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• v.56 no.6 s.105
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• pp.1-15
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• 2006

The purpose of this study is to present a new viewpoint in look by closely examining fashion and look through the past, present, and future. Specifically, the concept of time is studied and fashion is considered based upon the concept of time, finally, looks in fashion related to time are analysed. For this study, the literature of retro, modern, contemporary, futuristic looks that express the past, present, and future, and the classic look, the popularity of which transcends the concept of time was considered focusing on 20th century women's fashion. Design characteristics of these looks were analyzed utilizing photographs of collection images. The results of the study are as follows: first, although the retro look of different periods regularly rotate and appear, they change and develop in new and various ways depending on the Periodic background of the time they appear in, and thus show simultaneously the cyclical and straight nature of time. Second, the modern look includes not only the moaning of time concept of the present, but also of interpreting anew the formal significance of modernism in the present. In other words, because it cyclically repeats the times of past modern periods, the look can be said to focus on the cyclical nature of time. Third, the contemporary look is a style that expresses current time most exactly as it exists, and it can be said to be the look in which current time is most vividly expressed. Fourth, the time concept in futuristic look is a subjective time that selectively accommodates and expresses the objective time of the future which has not yet come, and that can be the look's most important point. Last, the classic look possesses an objective value that transcends the concept of time, and it keeps returning, showing the cyclical nature of time. The closer this study came to the present, it could be seen that a look communicated more complex meanings, Influenced by periodic phases such as diversification, individualization, and eclecticism, and that while it could not be defined as any one look, various elements were expressed eclectically by being mixed and matched with each other, and it could utilize more natural forms, colors, and materials.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.93-100
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• 2005

This study is to desist and implement an efficient microbial medical image retrieval system based on knowledge and content of them which can make use of more accurate decision on colony as doll as efficient education for new techicians. For this. re first address overall inference to set up flexible search path using rule-base in order U redure time required original microbial identification by searching the fastest path of microbial identification phase based on heuristics knowledge. Next, we propose a color ffature gfraction mtU, which is able to extract color feature vectors of visual contents from a inn microbial image based on especially bacteria image using HSV color model. In addition, for better retrieval performance based on large microbial databases, we present an integrated indexing technique that combines with B+-tree for indexing simple attributes, inverted file structure for text medical keywords list, and scan-based filtering method for high dimensional color feature vectors. Finally. the implemented system shows the possibility to manage and retrieve the complex microbial images using knowledge and visual contents itself effectively. We expect to decrease rapidly Loaming time for elementary technicians by tell organizing knowledge of clinical fields through proposed system.