• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.264-270
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• 2019

The purpose of this study is to examine the change of study crafting and nursing recognition after applying a documentary form of education to nursing students, and also to confirm the nursing students' satisfaction with the documentary style of education. The subjects of the study were 84 nursing students in their first year in the B area. The data collection period ran from March 11, 2019 to April 15, 2019. The collected data was analyzed using frequencies, percentages, means, standard deviations and paired t-test by employing the SPSS WIN 24.0 computer program. The program consisted of four parts and was operated for 3 hours and 30 minutes, and three domestic documentaries were applied. The study crafting of nursing students increased after the education but there was no statistical significance for this. The nursing recognition was significant (t=-4.49, p<.001) In detail, traditional image, social image and nursing prospect were significant (t=-2.13, p=.036; t=-5.09, p<.001; t=-4.17, p=<.001). Satisfaction with the use of documentaries averaged 4.54 points, as detailed items, the satisfaction with the learning method was 4.54, the satisfaction with the contents of the education was 4.62 points, the benefit was 4.56, the interest was 4.44 and the interest induction was 4.55 points. This study showed that documentaries could be used as a teaching and learning method because the documentaries had a positive effect on nursing students' recognition of nursing and satisfaction of education.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.186-192
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• 2009

In this paper, we represent the recognition method for human frontal upper body pose. In HCI(Human Computer Interaction) and HRI(Human Robot Interaction) when a interaction is established the human has usually frontal direction to the robot or computer and use hand gestures then we decide to focus on human frontal upper-body pose, The two main difficulties are firstly human pose is consist of many parts which cause high DOF(Degree Of Freedom) then the modeling of human pose is difficult. Secondly the matching between image features and modeling information is difficult. Then using Pictorial Model we model the human main poses which are mainly took the space of frontal upper-body poses and we recognize the main poses by making main pose database. using determined main pose we used the model parameters for particle filter which predicts the posterior distribution for pose parameters and can determine more specific pose by updating model parameters from the particle having the maximum likelihood. Therefore based on recognizing main poses and tracking the specific pose we recognize the human frontal upper body poses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1997-2003
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• 2009

Methods for effective musical score recognition and efficient editing of musical scores are demanded because functions of computers for researches on musical activities become more and more important parts in recent days. In the conventional methods for handling musical scores manually, there are weak points such as incorrect score symbols in input process and requirement of much time to adjust the incorrect symbols. And also there is another weak point that the scores edited by each application program can be remodified by a specific application program only. In this paper, we proposed a method for automatic musical score recognition of printed musical scores in order to make up for the weak points. In the proposed method, staffs in a scanned score image are eliminated by horizontal histogram, noises are removed by 4 directional edge tracking algorithm, and then musical score symbols are extracted by using Grassfire algorithm. The extracted symbols are recognized by hierarchical ART2 algorithm. In order to evaluate the performance of the proposed method, we used 100 musical scores for experiment. In the experiment, we verified that the proposed method using hierarchical ART2 algorithm is efficient.

• Journal of Distribution Science
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• v.9 no.4
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• pp.51-61
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• 2011

The concept of Corporate Social Responsibility (CSR) was first introduced sixty years ago in the academic field. However, the phrase CSR was not explicitly stated before the 1990s in Korean business and academic researches. Recently CSR is more considered a corporate strategy than a philanthropic donation. CSR comprises contributions to local communities as well as using environmentally beneficial and humane practices. Sponsoring is one available marketing tactic used in order to communicate with the market. This study of sponsorship has concentrated on developing brand asset by accessing potential values of sporting events or star-players. However, sponsorship includes providing funds or goods to non-profit institutions as well as sports or entertainment organizations. Accordingly corporate community-specific sponsorship is defined as firms offering to provide money, goods and/or services to individuals and/or institutions within a particular community, thus establishing an interdependent relationship between the partners aspiring to gain social and economic assets. National sponsorship is typically targeted toward commonly recognized individuals and/or organizations with the intent to maximize exposure of a sponsor's brand, and is known to positively affect brand equity(community-specific sponsorship is committed to a limited local area) that a firm could benefit from by gaining a specific asset. POSCO sponsors the Jukdo Market, locate dinthe city of Pohang, tohelp revive their traditional market. Inreturn, the Jukdo Market merchant suni on display sflags with the POSCO embleminfrontof stores with in the market intending to make shopper sand merchant saware of POSCO's sponsorship. POSCO has succeeded in acquiring public support from the citizens of Pohang. However, the economic effects resulting from the cooperative relationship between POSCO and the Jukdo Market have yet to be measured by any empirical research. The purpose of this study is to assess the economic effects created by the community-specific sponsorship from the groups of merchants and shoppers, measuring its influence on the corporate image and subsequent brand loyalty, as parts of brand equity. The result of the study shows that the community-specific sponsorship of POSCO of the Jukdo Market had different influences on its corporate image and the brand loyalty of shoppers and merchants. First, the merchant group who was more frequently exposed to POSCO's flag recognized the sponsorship of POSCO more than the shopper group, and, therefore, had a better image of the company. Second, the recognition of POSCO's sponsorship had a positive influence on its corporate image, and that positive corporate image had a positive effect on brand loyalty development. However, the recognition of the sponsorship did not have a direct influence on brand loyalty. The friendly corporate image developed by the recognition of the sponsorship consequently could have had an effect on brand loyalty. Therefore, companies should not relinquish investments to corporate image development if they require more brand loyalty. Third, the influence of corporate image on brand loyalty shows stronger results in the shopper group rather than in the merchant group. Psycho-graphic factors of shoppers and merchants might give rise to the difference between the two groups.

• Archives of design research
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• v.19 no.1 s.63
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• pp.79-88
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• 2006

A human face figure is a visual symbol of identity. Each different face per person is a critical information differentiating each person from others and it directly relates to individual identity. When we look back human history, historical change of recognition for a face led to the change of expression and communication media and it in turn caused many changes in expressing a face. However, there has not been no time period when people pay attention to a face more than this time. Technically, the advent of computer graphics opened new turning point in expressing human face figure. Especially, a visual image which can be produced, saved, and transferred in digital has no limitation in time and space, and its importance in communication is getting higher and higher. Among those visual image information, a face image in digital is getting more applications. Therefore, 3d (3-dimensional) expression of a face using computer graphics can be easily produced without any professional techniques, just like assembling puzzle parts composed of the shape of each part ands texture map, etc. This study presents a method with which a general visual designer can effectively express 3d type face by studying each producing step of 3d face expression and by visualizing case study based on the above-mentioned study result.

• Journal of KIISE
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• v.41 no.11
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• pp.921-926
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• 2014

Face detection refers to the process extracting facial regions in an input image, which can improve speed and accuracy of recognition or authorization system, and has diverse applicability. Since conventional works have tried to detect faces based on the whole shape of faces, its detection performance can be degraded by occlusion made with accessories or parts of body. In this paper we propose a method combining local feature descriptors and probability modeling in order to detect partially occluded face effectively. In training stage, we represent an image as a set of local feature descriptors and estimate a statistical model for normal faces. When the test image is given, we find a region that is most similar to face using our face model constructed in training stage. According to experimental results with benchmark data set, we confirmed the effect of proposed method on detecting partially occluded face.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.105-113
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• 2018

In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.65-70
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• 2023

In this paper, we propose a CCTV storage space securing model using YOLO v3. CCTV is installed and operated in various parts of society for disasters, disasters and safety such as crime prevention, fire prevention, and monitoring, and the number of CCTV is increasing and the quality of the video quality is improving. Due to this, as the number and size of image files increase, it is difficult to cope with the existing storage space. In order to solve this problem, we propose a model that detects specific objects in CCTV images using YOLO v3 library and deletes unnecessary frames by saving only the corresponding frames, thereby securing storage space by reducing the size of the image file, and thereby Periodic images can be stored and managed. After applying the proposed model, it was confirmed that the average image file size was reduced by 94.9%, and it was confirmed that the storage period was increased by about 20 times compared to before the application of the proposed model.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.687-692
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• 2018

In this paper, the development of an automotive thermal imaging camera providing image information for ADAS (Advanced Driver Assist System) and autonomous vehicles is described and an improved technique to enhance the details of the image is proposed. Thermal imaging cameras are used in various fields, such as the medical, industrial and military fields, for the purpose of temperature measurement and night vision. In automobiles, they are utilized for night vision systems. For their utilization in ADAS and autonomous vehicles, appropriate image resolution and enhanced detail are required for object recognition. In this study, a $640{\times}480$ resolution thermal imaging camera that can be applied to automobiles is developed and the BDE (Block-Range Detail Enhancement) technique is applied to improve the details of the image. In order to improve the image detail obtained in various driving environments, the block-range values between the target pixel and the surrounding 8 pixels are calculated and classified into 5 levels. Then, different factors are added or subtracted to obtain images with high utilization. The improved technique distinguishes the dark part of the image by the resulting temperature difference of 130mK and shows an improvement in the fine detail in both the bright and dark parts of the image. The developed thermal imaging camera using the improved detail enhancement technique is applied to a test vehicle and the results are presented.