• Journal of the Chungcheong Mathematical Society
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• v.11 no.1
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• pp.1-12
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• 1998

In this paper we shall define a relative Lusternik-Schnirelmann category of a subset in a space with respect to a map which generalizes the category of a space, the category of a map and the relative category of a subset in a space. We shall study some properties of the relative Lusternik-Schnirelmann category of a subset in a space with respect to a map and generalize many results of the above categories.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.3-4
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• 2010

Map integration usually involves matching the common spatial objects in different datasets. There have been recent studies on object matching using relative location as defined by spatial relationships between the object and its neighbor landmark. Therefore the landmark selection process is an important part of map integration using relative location. In this research, we describe an approach to determine landmarks automatically in different geospatial datasets.

• Journal of the Chungcheong Mathematical Society
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• v.13 no.1
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• pp.87-94
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• 2000

In this paper we shall de ne a concept of ${\pi}_1$-category for a map relative to a subset which is a generalization of both the category for a map and the ${\pi}_1$-category of a space, and study some properties of the ${\pi}_1$-category for a map relative to a subset.

• Communications of the Korean Mathematical Society
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• v.11 no.1
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• pp.245-252
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• 1996

The relative Nielsen number N(f;X,A) was introduced in 1986. It gives us a better, and ideally sharp, lower bound for the minimum number MF[f;X,A] of fixed points in the homotopy class of the map $f;(X,A) \to (X,A)$. Similarly, we also can think about the Nielsen map $f:(X,A) \to (X,A)$. Similarly, we also can be think about the Nielsen root theory. In this paper, we introduce a relative root Nielsen number N(f;X,A,c) of $f:(X,A) \to (Y,B)$ and show some basic properties.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.207-215
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• 2009

Recently, the research of the analysis of the crime spatial is increased by using the computer information technology and GIS (Geometric Information System) in order to prevent the urban crime so as to increase the urbanization rate. In this paper, a probability map formed by the raster is organized by the quantification of crime risk per the cell using the region property of the urban spatial information in the static environment. Also, a map of the risk probability is constructed based on the relative risk by the region property, the relative risk by the facility, the relative risk by the woody plant and the river, and so on. And, this integrated risk probability map is calculated by averaging the individual cell risk applied to the climatic influence and the seasonal factor. And, a probability map of the overall risk is generated by the interpretation key of the crime occurrence relative risk index, and so, this information is applied to the probability map quantifying the occurrence crime pattern. And so, in this paper, a methodology of the modeling and the simulation that this crime risk probability map is modified according to the passage of time are proposed.

• Communications of the Korean Mathematical Society
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• v.12 no.3
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• pp.701-707
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• 1997

In [8], we introduce the relative root Nielsen number N(f;X, A, c) for maps of pairs of spaces $f : (X, A) \to (Y, B)$. From it, we obtain some immediate consequences of the definition and illustrate it by some examples. We consider the question whether there exists a map $g : (X, A) \to (Y, B)$ homotopic to a given map $f : (X, A) \to (Y, B)$ which has precisely N(f;X, A, c) roots, that is, the minimum theorem for N(f;X, A, c).

• Communications of the Korean Mathematical Society
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• v.19 no.1
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• pp.159-167
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• 2004

In [1], a relative root Nielsen number $N_{rel}(f;\;c)$ is introduced which is a homotopy invariant lower bound for the number of roots at $c\;{\in}\;Y$ for a map of pairs of spaces $f\;:\;(X,\;A)\;{\rightarrow}\;(Y,\;B)$. In this paper, we obtain a minimum theorem for $N_{rel}(f;\;c)$ under some new assumptions on the spaces and maps which are different from those in [1].

• Journal of Digital Convergence
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• v.10 no.9
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• pp.351-356
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• 2012

This paper presents the 2D/3D conversion technique using depth map which is generated based on the haze and relative height cue. In cases that only the conventional haze information is used, errors in image without haze could be generated. To reduce this kind of errors, a new approach is proposed combining the haze information with depth map which is constructed based on the relative height cue. Also the gray scale image from Mean Shift Segmentation is combined with depth map of haze information to sharpen the object's contour lines, upgrading the quality of 3D image. Left and right view images are generated by DIBR(Depth Image Based Rendering) using input image and final depth map. The left and right images are used to generate red-cyan 3D image and the result is verified by measuring PSNR between the depth maps.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM

• Journal of the Korea Convergence Society
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• v.3 no.1
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• pp.13-17
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• 2012

In this paper, we propose method of generation of depth map using vanishing line and texture. vanishing line is generated by parallel lines in image. For generate vanishing line, show boundary of particular angle through Gabor Filter and extract line through Hough Transform. Initial Depth Map is estimated based on vanisihng line and combine Relative Depth map that generated using Texture Cue. The proposed algorithm advanced due to combine Initial Depth Map and Relative Depth Map.