• The Journal of the Korea Contents Association
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• v.3 no.2
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• pp.1-10
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• 2003

A semiotic approach to modem visual arts has emerged since French post-structuralism was introduced to Anglo -American academics by "deconstruction" or "postmodemi sm." It views a work of art as a sign, which is its math odical assumption and, at the same time, makes its appli cation more accessible. In the milieu of modem visual arts' effort to be intimate with general audience, modem art photography is now fared with the request to be a familar and universal domain, instead of being left only in photo books as artistic and academic achievements. More specifically, various photo images puter graphics to such megaexhibitions as "Gwangju Biennale,. "Media City Seo ul," and "Pusan International Art Festival," are main objects of study. A coherent and scientific analysis of visual semitotics is still on the way, however, it is an urgent task how to read and interpret a photo image with multiple meanings. This study argues that visual seniotics can be a powerful tool to enhance the understanding of art photography. After all, seniotics is a product of age； we live in the age of legibility, that is, of reading the work of art well as the social events and phenomena. art well as the social events and phenomena.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.271-274
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• 2006

This paper deals with a similarity decision method between the shape of hand-postures and their structures to improve performance of the vision-based hand-posture recognition system. Hand-posture recognition by vision sensors has difficulties since the human hand is an object with high degrees of freedom, and hence grabbed images present complex self-occlusion effects and, even for one hand-posture, various appearances according to viewing directions. Therefore many approaches limit the relative angle between cameras and hands or use multiple cameras. The former approach, however, restricts user's operation area. The latter requires additional considerations on the way of merging the results from each camera image to get the final recognition result. To recognize hand-postures, we use both of appearance and structural features and decide the similarity between the two types of features by learning.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.775-784
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• 2019

Image stitching is a technique for solving the problem of narrow field of view of a camera by composing multiple images. Recently, as the use of content such as Panorama, Super Resolution, and 360 VR increases, the need for faster and more accurate image stitching technology is increasing. So far, many algorithms have been proposed to satisfy the required performance, but the objective evaluation method for measuring the accuracy has not been standardized. In this paper, we present the problems of PSNR and SSIM(Structural similarity index method) measurement methods and propose a Local Differential Pixel Mean method. The LDPM evaluation method that includes geometric similarity and brightness measurement information is proved through a test, and the advantages of the evaluation method are revealed through comparison with SSIM.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.91-99
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• 2019

In this paper, we propose partial denoising boundary matching based on an index for faster matching in very large image databases. Attempts have recently been made to convert boundary images to time-series with the objective of solving the partial denoising problem in boundary matching. In this paper, we deal with the disk I/O overhead problem of boundary matching to support partial denoising in a large image database. Although the solution to the problem superficially appears trivial as it only applies indexing techniques to boundary matching, it is not trivial since multiple indexes are required for every possible denoising parameters. Our solution is an efficient index-based approach to partial denoising using $R^*-tree$ in boundary matching. The results of experiments conducted show that our index-based matching methods improve search performance by orders of magnitude.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.35-43
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• 2013

This paper suggests a way to implement a system that broadcasts compressed JPEG images obtained from a webcam to multiple users on their smartphones. The system was implemented to maintain a suitable image quality so that things are identifiable while using the minimal amount of data, in order to deliver as many frames as possible to as many users as possible. Also, the suggested way was applied and various tests were done, including on: the performance of the server that provides image information; the performance of the client that receives the image information and displays it on the smartphone; and the max. number of simultaneous users supported by the system. When a broadcasting system is implemented using webcams and smartphones, the results of this paper can be used in estimating the suitable system parameters depending on network performance, including the max. number of simultaneous clients supported, the client smartphone performance required, and the number of frames that can be transmitted per second.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.987-999
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• 2021

Recently, Federated learning has become an issue due to privacy invasion caused by data. Federated learning is safe from privacy violations because it does not need to be collected into a server and does not require learning data. As a result, studies on application methods for utilizing distributed devices and data are underway. However, Federated learning is no longer safe as research on the reconstruction attack to restore learning data from gradients transmitted in the Federated learning process progresses. This paper is to verify numerically and visually how well data reconstruction attacks work in various data situations. Considering that the attacker does not know how the data is constructed, divide the data with the class from when only one data exists to when multiple data are distributed within the class, and use MNIST data as an evaluation index that is MSE, LOSS, PSNR, and SSIM. The fact is that the more classes and data, the higher MSE, LOSS, and PSNR and SSIM are, the lower the reconstruction performance, but sufficient privacy invasion is possible with several reconstructed images.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

• Safety and Health at Work
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• v.11 no.4
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• pp.485-490
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• 2020

Background: Meniscal tears are among the major risk factors for knee osteoarthritis progression. This study aimed to investigate the relationship between meniscal tears and work-related factors in the farming occupation. Methods: The participants included 486 farmers (238 men and 248 women), aged 40-69 years, who were among the 550 farmers registered in the Korea Farmer's Knee Cohort (KFKC). Data such as those on gender, age, body mass index (BMI), mechanical axis, cumulative heavy-lifting working time (CLWT), cumulative squatting working time (CSWT), and previous knee injury history were collected from the questionnaire, along with whole leg radiographic findings. Two radiologists assessed the magnetic resonance images of both knees to confirm the presence of meniscal tears. The factors related to meniscal tears were analyzed by multiple logistic regression. Results: A total of 54.5% of the farmers (48.7% of men and 60.1% of women) had meniscal tears. These tears were associated with gender, age, and BMI. We also identified an association between meniscal tears and CSWT, an especially important factor in farming [10,000-19,999 working hours, odds ratio = 2.16, 95% confidence interval (CI): 1.14-4.07, ≥20,000 working hours, odds ratio = 2.35, 1.45-3.80]. However, mechanical axis, knee injury history, and CLWT were not significantly related to meniscal tears. Conclusion: This study's findings show that squatting for long periods, as an occupational factor, is related to meniscal tears.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.4
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• pp.40-50
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• 2019

In recent years, it is important to visualize an accurate human body model for the low-end system in the medical imaging field where augmented reality technology and virtual reality technology are used. Decreasing the geometry of a model causes a difference from the original shape and considers the difference as an error. So, the error should be minimized while reducing geometry. In this study, the organ areas of a human body in the tomographic images such as CT or MRI is segmented and 3D geometric model is generated, thereby implementing the reconstruction method of multiple resolution level-of-detail model. In the experiment, a virtual reality platform was constructed to verify the shape of the reconstructed model, targeting the spine area. The 3D human body model and patient information can be verified using the virtual reality platform.