• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.57-69
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• 2001

This study propose a image processing system of unsupervised analysis. This system integrates low-level segmentation and high-level classification. The segmentation and classification are conducted respectively with and without spatial constraints on merging by a hierarchical clustering procedure. The clustering utilizes the local mutually closest neighbors and multi-window operation of a pyramid-like structure. The proposed system has been evaluated using simulated images and applied for the LANDSATETM+ image collected from Youngin-Nungpyung area on the Korean Peninsula.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.40.3-40.3
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• 2019

Our recent observations of the Sun through strong spectral lines have revealed several important properties of the three-minute umbral oscillations inside sunspots -- the oscillations of intensity and Doppler velocity with periods of 2 to 3 minutes. The oscillations usually occur in the form of a time series of oscillation packets each of which lasts 10 to 20 minutes, not as continuous trains. Each oscillation packet is characterized by a singly peaked power spectrum of velocity oscillation. The oscillations propagate in the vertical direction from the photosphere to the corona. In the upper chromosphere, they develop into shocks that eventually collide with the transition region. When shocks propagate along a highly inclined direction, the merging of two successive shocks can take place. Once they enter the corona, they change to linear compressional waves. In the image plane, the three-minute oscillations propagate with high speeds in the transverse direction as well, usually propagating radially outwards from a point, and sometimes accompanying spiraling patterns of Doppler velocity. These observational properties can be theoretically explained by postulating the spatio-temporally localized source of fast MHD waves at a depth of about 2000 km below the surface, the excitation of slow MHD waves via mode conversion near the photosphere, and the resonance of the slow waves in the photospheric layer below the temperature minimum, and the nonlinear development of slow waves in the chromosphere.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.30.2-31
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• 2020

We present the properties of circumnuclear gas associated with the AGN located in the center of Abell 1644-South. A1644-S is the main cluster in a merging system, which is also known for gas sloshing in its core as seen in X-ray. The X-ray emission of A1644-S shows a rapidly declining profile, indicating the presence of cooling gas flow. This flow of cool gas may fuel the supermassive black hole embedded in the brightest cluster galaxy, leading to the activation of the central AGN. Indeed, we find a parsec-scale bipolar jet feature in the center of A1644-S in our recent KaVA observation, which implies that its central AGN is likely to have been (re)powered quite recently. In order to verify the hypothesis that cooling gas flow in the cluster core can (re)activate the central AGN, we probe the cold gas properties of the central 1 kpc region of A1644-S using the archival VLA and ALMA data. Based on the spatially resolved morphology and kinematics of HI and CO gas, we challenge to identify inflow/outflow gas streams and clumps. We study the role of circumnuclear cool gas in fueling the centrally located cluster AGN in the cool-core environment. We also discuss how the feedback due to the (re)powered AGN affects the surrounding medium.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.54.3-54.3
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• 2021

The Coma cluster serves as an ideal laboratory to study the cluster assembly history. It is known as a typical example of relaxed galaxy clusters. However, recent X-ray, radio and optical observations revealed a number of substructures in Coma. The NGC 4839 group is an interesting substucture in the sense that it is overlappled with the X-ray bright component in the south-west region. Recent hydrodynamical simulations in the literature suggest that the NGC 4839 group came from the north-east direction of Coma, passed the apocenter about 1 Gyr ago, and started a second infall to the Coma core recently. Interestingly a number of E+A galaxies are located along the filament connecting the NGC 4839 group and the Coma core. We are surveying a wide area covering the NGC 4839 group to search for globular clusters and use them to investigate any connection between the globular clusters and the merger scenario of the NGC 4839 group. We utilized Subaru Hyper Suprime-Cam archival images of two circular fields with diameter ~1.8 deg, covering the Coma core and the NGC 4839 group. We discuss the results with regard to the formation history of the NGC 4839 group.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.37-44
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• 2006

In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.610-616
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• 2012

In this paper, we present a method to detect an object such as ship, rock and buoy from sea IR image for the safety navigation. To this end, we do the image smoothing first and the apply watershed algorithm to segment image into subregions. Since watershed algorithm almost always produces over-segmented regions, it requires posterior merging process to get meaningful segmented regions. We propose an efficient merger algorithm that requires only two times of direct access to the pixels regardless of the number of regions. Also by analyzing IR image obtained from sea environments, we could find out that most horizontal edge come out from object regions. For the given input IR image we extract horizontal edge and eliminate isolated edges produced from background and noises by adopting morphological operator. Among the segmented regions, the regions that have horizontal edges are extracted as final results. Experimental results show the adequacy of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.115-126
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• 2012

The artificial captions appearing in videos include information that relates to the videos. In order to obtain the information carried by captions, many methods for caption extraction from videos have been studied. Most traditional methods of detecting caption region have used one frame. However video include not only spatial information but also temporal information. So we propose a method of detection caption region using temporal and spatial information. First, we make improved Text-Appearance-Map and detect continuous candidate regions through matching between candidate-regions. Second, we detect disappearing captions using disappearance test in candidate regions. In case of captions disappear, the caption regions are decided by a merging process which use temporal and spatial information. Final, we decide final caption regions through ANNs using edge direction histograms for verification. Our proposed method was experienced on many kinds of captions with a variety of sizes, shapes, positions and the experiment result was evaluated through Recall and Precision.

• 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.