• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.41-44
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• 2003

First, we investigate some properties of fuzzy compactness. Second, we introduce the concept of fuzzy local compactness in fuzzy topological space and study some of its properties. Finally, we investigate some relations between F-compactness in fuzzy topological spaces and one in fuzzy hyperspaces.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.89-97
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• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2737-2753
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• 2013

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

• The Mathematical Education
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• v.13 no.2
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• pp.27-28
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• 1974

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.29-58
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• 2022

Recently, Korea has been experiencing a problem of population decline, therefore the transition to a compact spatial structure is being urged. However, what is required is not just physical compression, but a compact city that also considers connectivity, in view of the changes in today's demographic and industrial structure. From this point of view, this study measures the compressibility of domestic cities suffering from extinction risk due to low birth rates, aging population, and population decline, and examined the spatial structure characteristics. In addition to the compressibility evaluation index used in previous studies, the compressibility of six indicators (population, land use, service accessibility, transport accessibility, connectivity, and concentration) was compared and analyzed, and a comprehensive compression index was calculated. The analysis results, based on the comprehensive compression index, classified 2.3% cities in the first grade, 4.6% in the second grade, 16.09% in the third grade, 43.68% in the fourth grade, and 33.33% in the fifth grade areas. Currently, the urban characteristics affecting the compactness index differ from region to region. Therefore, it is necessary to establish measures and policies for extinction risk considering the influence of each region's compactness index. This study is meaningful in that connectivity was considered using spatial syntax, and the compactness of cities at risk of extinction was compared and analyzed quantitatively. It is expected that this study will be used as basic data to establish the direction and action strategy for extinction risk cities.

• East Asian mathematical journal
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• v.15 no.1
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• pp.91-103
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• 1999

• Journal of the Korean Mathematical Society
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• v.36 no.6
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• pp.1047-1059
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• 1999

Let $C(X)(C_{K}(X))$ denote the hyperspace of all nonempty closed connected subsets (subcontinua) of a locally compact Haus-dorff space X with the Fell topology. We prove that the following statements are equivalent: (1) X is locally connected. (2) C(X) is locally connected,. (3) C(X) is locally connected at each $E{\in}C_{k}(X).(4) C_{k}(X)$ is locally connected.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.159-162
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• 2000

Selecting locally optimum thresholds, based on optimizing a criterion composed of the area variation rate and the compactness of the segmented shape, is presented. The method is shown to have the shape-resolving property in the subtraction image, so that overlapped objects may be resolved into bright and dark evidences characterizing each object. As an application a vehicle detection algorithm robust to the operating conditions could be realized by applying simple merging rules to the geometrically correlated bright and dark evidences obtained by this local thresholding.

• Kyungpook Mathematical Journal
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• v.32 no.1
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• pp.31-43
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• 1992

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.59.4-60
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• 2016

Recent galaxy simulations suggest several scenarios in which the inner structure of late-type galaxies (LTGs) is linked to global quenching. Exactly what mechanism governs the bulge quenching is, however, still under debate due to the lack of observational clues. In this study, we utilize a sample of ~1,300 LTGs in the local universe (0.02 < z < 0.2) from SDSS 7, and classify them into star-forming, AGN-hosting, and composite types and into barred and unbarred galaxies. We also examine each subgroup's specific star forming rate (sSFR), stellar mass and compactness using a data set matched with the advanced sSFR catalog by Chang et al. (2015). We find that while star-forming and composite galaxies show no detectable difference between barred and unbarred galaxies, barred AGNs have much lower sSFR than unbarred AGNs at given stellar mass and compactness, Such tendency is stronger for more massive and/or more concentrated galaxies. The results indicate that most AGN-driven quenching is triggered by growth of the bar structure, consistent with the previous simulations of bars.