• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.205-210
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• 2008

A new approach is proposed for restoration of corrupted regions and segmentation in natural text images. The challenge is to fill in the corrupted regions on the basis of color feature analysis by second order symmetric stick tensor. It is show how feature analysis can benefit from analyzing features using tensor voting with chromatic and achromatic components. The proposed method is applied to text images corrupted by manifold types of various noises. Firstly, we decompose an image into chromatic and achromatic components to analyze images. Secondly, selected feature vectors are analyzed by second-order symmetric stick tensor. And tensors are redefined by voting information with neighbor voters, while restore the corrupted regions. Lastly, mode estimation and segmentation are performed by adaptive mean shift and separated clustering method respectively. This approach is automatically done, thereby allowing to easily fill-in corrupted regions containing completely different structures and surrounding backgrounds. Applications of proposed method include the restoration of damaged text images; removal of superimposed noises or streaks. We so can see that proposed approach is efficient and robust in terms of restoring and segmenting text images corrupted.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.239-248
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• 2010

Leukocyte in blood smear image provides significant information to doctors for diagnosis of patient health status. Therefore, it is necessary step to separate leukocyte from blood smear image among various blood cells for early disease prediction. In this paper, we present a saliency map and stepwise region merging based leukocyte segmentation method. Since leukocyte region has salient color and texture, we create a saliency map using these feature map. Saliency map is used for sub-image separation. Then, clustering is performed on each sub-image using mean-shift. After mean-shift is applied, stepwise region-merging is applied to particle clusters to obtain final leukocyte nucleus. The experimental results show that our system can indeed improve segmentation performance compared to previous researches with average accuracy rate of 71%.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.25-33
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• 2010

We propose a retouching method that converts a general photography to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform DoG(Difference of Gradient) edge extraction and mean shift segmentation respectively from the bilateral filtered image. The DoG edge extraction is performed using luminance component of the image whose RGB color space is transformed into CIELAB space. Experimental result shows that our method can be applied to various types of image and bring better result, especially against the photo taken in daylight.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.975-986
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• 2022

Deep learning is attracting great attention, showing excellent performance in image processing, but is vulnerable to adversarial attacks that cause the model to misclassify through perturbation on input data. Adversarial examples generated by adversarial attacks are minimally perturbated where it is difficult to identify, so visual features of the images are not generally changed. Unlikely deep learning models, people are not fooled by adversarial examples, because they classify the images based on such visual features of images. This paper proposes adversarial attack detection method using Symbolic Representation, which is a visual and symbolic features such as color, shape of the image. We detect a adversarial examples by comparing the converted Symbolic Representation from the classification results for the input image and Symbolic Representation extracted from the input images. As a result of measuring performance on adversarial examples by various attack method, detection rates differed depending on attack targets and methods, but was up to 99.02% for specific target attack.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.149-159
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• 2021

This study wanted to analyze the types of recognition of messages expressed in clothing for women in their 20s who wear a lot of clothing and fashion products with text. It was intended to provide basic data necessary for the production of typography clothing and fashion products by considering the subjective evaluation of how women in their 20s type the characters expressed in fashion and the characteristics of each type. This study was conducted with the Q method, and the QUANL pc program was used for analysis. Type I thought that letters were a design element and fashion, and the characters expressed in clothes were recognized as images. Type 2 thought it was important that the characters expressed in the clothing were recognized as messages, and that the characters had social messages and period reflections. Type 3 preferred that letters be combined with casual clothes and valued the formability of the characters. Type 4 preferred characters to represent brands and liked to be placed in large positions. In the future, it is thought that additional research by various age groups and genders and detailed research should be conducted to identify differences in font, color, and sentence length.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.180-182
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• 2022

As various crimes such as theft, assault, and sex crimes are increasing, each local government is installing CCTVs to prevent them, and operating and managing control centers for emergency response. When the control center detects a dangerous situation in the field, it responds immediately in connection with the police or 911. However, since it is managed by humans, the response speed is anomalous and the reality is that it is mainly used for post-processing. Therefore, through the artificial intelligence LED signage, it notifies the emergency situation at the site, and it serves as a warning function before getting help from passers-by or an accident occurs. In this paper, we design and research a warning system such as changing the lighting color of the LED signboard or making a sound by reflecting the artificial intelligence algorithm. We intend to contribute to public safety and social safety through this study.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.