• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.77-84
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• 2008

This paper proposes a new approach to eliminate the reflectance component for the localization of text in natural scene images. Natural scene images normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while Preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. In the normal image, we acquired the text region without additional processing. Otherwise we removed light reflecting from the object using homomorphic filtering in the polarized image. And then this decided the each text region based on the color merging technique and the Saliency Map. Finally, we localized text region on these two candidate regions.

• Journal of Platform Technology
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• v.9 no.4
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• pp.71-78
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• 2021

Self-driving vehicles are increasing as new alternatives to solving problems such as human safety, environment and aging. And such technology development has a great ripple effect on other industries. However, various problems are occurring. The number of casualties caused by self-driving is increasing. Although the collision of fixed obstacles is somewhat decreasing, on the contrary, the technology by active obstacles is still insignificant. Therefore, in this study, in order to solve the core problem of self-driving vehicles, we propose a method of extracting active obstacles on the road. First, a center scene is extracted from a continuous image. In addition, it was proposed to extract activity obstacles using activity size and activity repeatability information from objects included in the center scene. The center scene is calculated using region segmentation and merging. Based on these results, the size of the frequency for each pixel in the region was calculated and the size of the activity of the obstacle was calculated using information that frequently appears in activity. Compared to the results extracted directly by humans, the extraction accuracy was somewhat lower, but satisfactory results were obtained. Therefore, it is believed that the proposed method will contribute to solving the problems of self-driving and reducing human accidents.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.95-101
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• 2023

Colmap is one of the innovative artificial intelligence technologies, highly effective as a tool in 3D reconstruction tasks. Moreover, it excels at constructing intricate 3D models by utilizing images and corresponding metadata. Colmap generates 3D models by merging 2D images, camera position data, depth information, and so on. Through this, it achieves detailed and precise 3D reconstructions, inclusive of objects from the real world. Additionally, Colmap provides rapid processing by leveraging GPUs, allowing for efficient operation even within large data sets. In this paper, we have presented a method of collecting 2D images of traditional Korean towers and reconstructing them into 3D models using Colmap. This study applied this technology in the restoration process of traditional stone towers in South Korea. As a result, we confirmed the potential applicability of Colmap in the field of cultural heritage restoration.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.124-136
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• 2019

Autonomous driving at intersections requires assistance by exchanging traffic information between traffic objects due to the intersection of various vehicles and complicated driving environment. For this reason, traffic information exchange between adjacent intersections is required, but the node ID representing the intersection in the Korean standard node link system have limitations in updating intersections and identifying location information of intersections through IDs due to the configuration system including serial numbers. In this paper, we designed a coordinate-based intersection ID configuration system created by processing and merging two-dimensional coordinates of intersections to include location information in the intersection ID. In order to verify the applicability of the proposed intersection ID, we applied a new intersection ID to domestic intersections and confirmed that there are no duplicate values. Coordinate-based intersection ID reduces data size by 60% compared to existing node ID, and enables spatial queries such as searching for nearby intersections and extracting intersections in specific areas in the form of boxes without GIS tools. Therefore, coordinate-based intersection ID is expected to be more scalable and utilized than existing node ID.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.165-172
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• 2013

Convergence is increasingly prevalent in the IT world which generally refers to the combination of two or more different technologies in a single device. Especially, the spectrum scarcity is becoming a big issue because there are exponential growth of broadcasting and communication systems in the spectrum demand. Cognitive radio (CR) is a convergence technology that is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. However, the very process of convergence is likely to expose significant security issues due to the merging of what have been separate services and technologies and also as a result of the introduction of new technologies. The main purpose of this research is focused on devising an adaptive security framework based on carousel for CR networks as a distinct telecommunication convergence application, which are still at the stage of being developed and standardized with the lack of security concerns. The framework uses a secure credential, named as carousel, initialized with the location related information from objects position, which is used to design security mechanisms for supporting privacy and various securities based on it. The proposed adaptive security framework could be used as a security building block for the CR network standards and various convergence applications.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.