• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.59-67
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• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.4
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• pp.81-93
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• 2015

The research was conducted with awareness of cultural uniqueness in each country and similarities and universality among countries. On that account, it raised questions about the idea that social well-being can be achieved only materialistic consumption and its cultural symbolism can lead to incongruity in social hierarchy and then dysfunctions in a counter way. On the other hand, the value of well-being can be achieved by many different ways and within one's limited budget as well as material consumption. This paper provides basic information about the development of essential wellbeing products and strategies for building market segmentation in the industry. The research also provides policy makers with the direction of welfare policy in our society to establish a foundation of creating conditions for true well being. This paper will be helpful in fulfilling well-being in our whole society.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1262-1270
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• 2020

Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

This paper proposes a novel hidden Markov model (HMM)-based gesture recognition method and applies it to an HCI to control a computer game. The novelty of the proposed method is two-fold: 1) the proposed method uses a continuous streaming of human motion as the input to the HMM instead of isolated data sequences or pre-segmented sequences of data and 2) the gesture segmentation and recognition are performed simultaneously. The proposed method consists of a single HMM composed of thirteen gesture-specific HMMs that independently recognize certain gestures. It takes a continuous stream of pose symbols as an input, where a pose is composed of coordinates that indicate the face, left hand, and right hand. Whenever a new input Pose arrives, the HMM continuously updates its state probabilities, then recognizes a gesture if the probability of a distinctive state exceeds a predefined threshold. To assess the validity of the proposed method, it was applied to a real game, Quake II, and the results demonstrated that the proposed HMM could provide very useful information to enhance the discrimination between different classes and reduce the computational cost.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.159-173
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• 2019

The purpose of this study is to examine the effect of service quality of banks on customer satisfaction and recommendation intention through empirical analysis. In particular, the focus was on the differences of the causal effect between PB(Private Banking) customers and regular customers. For this study, two groups were surveyed and 428 valid questionnaires were analyzed. The hypothesis was tested with a structural equation model using AMOS 23.0. As a result, empathy, reliability and tangibles of bank service quality had a positive(+) effect on customer satisfaction. However, responsiveness and assurance were not statistically significant. On the other hand, customer satisfaction has a positive effect on recommendation intention. This study was conducted to compare the two groups, PB customers and regular customers, and found a significant difference. In the PB customers group, tangibles had a positive effect on customer satisfaction, but no other factors were supported. On the other hand, in the regular customers group, empathy and reliability had a positive effect on customer satisfaction while responsiveness, assurance, and tangibles were not supported. Customer satisfaction were analyzed to have a positive influence on recommendation intention in both groups. These findings are academically significant by applying the SERVQUAL factors to banking services and revealing the differences between the PB customers and regular customers. In practice, it is meaningful in that it provided banks with the insights needed for future segmentation and management of customer groups.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.423-430
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• 2013

Recently, the personal identification technologies using vein pattern of back of the hand, palm, and finger have been developed actively because it has the advantage that the vein blood vessel in the body is impossible to damage, make a replication and forge. However, it is difficult to extract clearly the vein region from captured vein images through common image prcessing based region segmentation method, because of the light scattering and non-uniform internal tissue by skin layer and inside layer skeleton, etc. Especially, it takes a long time for processing time and makes a discontinuity of blood vessel just in a image because it has non-uniform illumination due to use a locally different adaptive threshold for the binarization of acquired finger-vein image. To solve this problem, we propose illumination normalization based fast method for extracting the finger-vein region. The proposed method has advantages compared to the previous methods as follows. Firstly, for remove a non-uniform illumination of the captured vein image, we obtain a illumination component of the captured vein image by using a low-pass filter. Secondly, by extracting the finger-vein path using one time binarization of a single threshold selection, we were able to reduce the processing time. Through experimental results, we confirmed that the accuracy of extracting the finger-vein region was increased and the processing time was shortened than prior methods.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.96-99
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• 2004

The objective of this study is to assess attenuation correction algorithms utilized in a multipurpose whole-body GSO PET scanner. Four different types of phantoms were tested using different types of attenuation correction techniques. FOV (Field of View) of 256mm was used for brain PET imaging. For compensating attenuation, transmission data of a $^{137}$Cs point source were acquired after the F-18 emission source was infused to the phantoms. Scatter correction were peformed. Reconstructed images of the phantoms were assessed. In addition, reconstructed images of a normal subject were compared and assessed by nuclear medicine physicians. As a result, decreased intensity at the central portion of the attenuation map with cylindrical phantom was noticed during use of the measured attenuation correction. On the other hand, segmentation or remapping attenuation correction provided uniform phantom image. the images reconstructed from the clinical brain data explained the attenuation of a skull, at though reconstructed images of the phantoms couldn't explain it. in conclusion, the complicated and improved attenuation correction methods were required to obtain the better accuracy of the quantitative brain PET images. Our study will be useful in improving quantitative brain PET imaging modalities with attenuation correction of $^{137}$Cs transmission source.