• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• The Korean Journal of Franchise Management
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• v.8 no.1
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• pp.19-30
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• 2017

Purpose - As environmental issues, along with the growth of companies, are accelerating, social interests in eco-friendly management that requires corporate social role and responsibility are increasing. The eco-friendly management activity reflects the changes in environmental awareness of consumers. Therefore, the eco-friendly images of companies influence consumers, and the establishing of eco-friendly management strategy has become a very important factor in the greenmarket. In this regard, this study examined the impacts of green leadership and environmental attractiveness on strategic environmental marketing, tactical environmental marketing, and environmental performance towards the employees of franchisee headquarters. Research design, data, and methodology - The survey was conducted towards the 800 headquarters among 2,600 brands that are registered with the Fair Trade Commission of Korea by mail. Among the total of 162 questionnaires collected, 7 respondents were excluded for their incompletion, and thus 155 responses were used in this study. The data were analyzed with SPSS 21.0 and SmartPLS 3.0. Frequency analysis was carried out to understand the general characteristics of the subjects, and confirmatory factor analysis to measure the reliability and validity of the measurement. Correlation analysis was conducted to identify the correlation between constructs, and structural equation modeling to examine the structural relationships among the constructs. Result and Conclusions - First, green leadership had a positive impact on strategic environmental marketing, tactical environmental marketing, and environmental performance. Second, environmental attractiveness had a positive effect on strategic environmental marketing, tactical environmental marketing, and environmental performance. Finally, strategic environmental marketing and tactical environmental marketing had positive impacts on environmental performance. This study can be recognized for proposing new perspectives on eco-friendly management strategy for firms to be able to win competitive superiority and performance by embedding awareness of the importance of environmental market and suggesting practical implications on understanding of environmental attractiveness, strategies and tactics of environmental management, and environmental performance in the franchise industry.

• Journal of the Korean Society of Food Culture
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• v.25 no.6
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• pp.746-754
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• 2010

Rural tourism is primarily a domestic tourism activity with visitors traveling to non-urban areas. The development of local and regionally denominate food is a way to distinguish agricultural production and to promote rural tourism. Therefore, this study addressed how utilizing regional agricultural products results in increasing the intention of tourists to revisit an area. The purposes of this study were 1) to identify the image and motives for visiting Yesan, 2) to determine the importance of purchasing intention and the regional menu produced from local agricultural/special products, and 3) to identify the impact of purchasing local agricultural/special products and regional menus on the intention to revisit. A total of 202 usable questionnaires were collected at Ducksan Hotsprings and Suduck Temple in Yean area, which are known tourist attractions. The major findings obtained were as follows: First, Yesan was considered a relaxing place ($3.46{\pm}1.09$), which was the highest ranked image score for a tourist attraction. Second, the highest ranked motive for visiting Yesan was to rest ($3.77{\pm}1.18$). According to these findings, Yesan is a relaxing place, as it is a rural area with no known defined attractions. Third, most tourists (78.7%) recognized the apple as a local agricultural/special product. The intentions to purchase local agricultural/special products and the need for regional dishes in the local restaurant was higher than average. Tourists showed interests ($3.88{\pm}1.16$) in eating regional dishes made with local agricultural/special products at the restaurants. Fourth, a significant impact of purchasing local agricultural/special products and the regional menu was observed on the intention to revisit (p<0.000). The results indicate that it is very important to develop proper regional menus that concur with images of the location and the regional farming products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.6-8
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• 2022

Many systems that use images through object recognition using deep learning have provided various solutions beyond the existing methods. Many studies have proven its usability, and the actual control system shows the possibility of using it to make people's work more convenient. Many studies have proven its usability, and actual control systems make human tasks more convenient and show possible. However, with hardware-intensive performance, the development of models is facing some limitations, and the ease with the use and additional utilization of many unupdated models is falling. In this paper, we propose how to increase utilization and accuracy by providing additional information on the emotional regions of colors and objects by utilizing learning and weights from HSV color histograms of local image data recognized after conventional stereotyped object recognition results.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.90-96
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• 2023

Securing large amounts of training data in deep learning neural networks, including convolutional neural networks, is of importance for avoiding overfitting phenomenon or for the excellent performance. However, securing labeled training data in deep learning neural networks is very limited in reality. To overcome this, several augmentation methods have been proposed in the literature to generate an additional large amount of training data through transformation or manipulation of the already acquired traing data. However, unlike training data such as images and texts, it is barely to find an augmentation method in the literature that additionally generates bio-signal training data for convolutional neural network based human activity recognition. Thus, this study proposes a simple but effective augmentation method of bio-signal training data for convolutional neural network based human activity recognition. The usefulness of the proposed augmentation method is validated by showing that human activity is recognized with high accuracy by convolutional neural network trained with its augmented bio-signal training data.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.

• Journal of the Korean Society of Radiology
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• v.85 no.2
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• pp.270-296
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• 2024

Coronary CT angiography (CCTA) is recognized for its role as a gatekeeper for invasive coronary angiography in patients suspected of coronary artery disease because it can detect significant coronary stenosis with high accuracy. However, heavy plaque in the coronary artery makes it difficult to visualize the lumen, which can lead to errors in the interpretation of the CCTA results. This is primarily due to the limited spatial resolution of CT scanners, resulting in blooming artifacts caused by calcium. However, coronary stenosis with high calcium scores often requires evaluation using CCTA. Technological methods to overcome these limitations include the introduction of high-resolution CT scanners, the development of reconstruction techniques, and the subtraction technique. Methods to improve reading ability, such as the setting of appropriate window width and height, and evaluation of the position of calcified plaque and residual visibility of the lumen in cross-sectional images, are also recommended.

• Cartoon and Animation Studies
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• s.47
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• pp.1-29
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• 2017

360-degree virtual reality has been a technology that has been available for a long time and has been actively promoted worldwide in recent years due to development of devices such as HMD (Head Mounted Display) and development of hardware for controlling and executing images of virtual reality. The production of the 360 degree VR requires a different mode of production than the traditional video production, and the matters to be considered for the user have begun to appear. Since the virtual reality image is aimed at a platform that requires enthusiasm, presence and interaction, it is necessary to have a suitable cinematography. In VR, users can freely enjoy the world created by the director and have the advantage of being able to concentrate on his interests during playing the image. However, the director had to develope and install the device what the observer could concentrate on the narrative progression and images to be delivered. Among the various methods of transmitting images, the director can use the composition of the short. In this paper, we will study how to effectively apply the technique of directing through the composition of this shot to 360 degrees virtual reality. Currently, there are no killer contents that are still dominant in the world, including inside and outside the country. In this situation, the potential of virtual reality is recognized and various images are produced. So the way of production follows the traditional image production method, and the shot composition is the same. However, in the 360 degree virtual reality, the use of the long take or blocking technique of the conventional third person view point is used as the main production configuration, and the limit of the short configuration is felt. In addition, while the viewer can interactively view the 360-degree screen using the HMD tracking, the configuration of the shot and the connection of the shot are absolutely dependent on the director like the existing cinematography. In this study, I tried to study whether the viewer can freely change the cinematography such as the composition of the shot at a user's desired time using the feature of interaction of the VR image. To do this, 3D animation was created using a game tool called Unreal Engine to construct an interactive image. Using visual scripting of Unreal Engine called blueprint, we create a device that distinguishes the true and false condition of a condition with a trigger node, which makes a variety of shorts. Through this, various direction techniques are developed and related research is expected, and it is expected to help the development of 360 degree VR image.