• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1562-1578
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• 2020

Vector data compression algorithm can meet requirements of different levels and scales by reducing the data amount of vector graphics, so as to reduce the transmission, processing time and storage overhead of data. In view of the fact that large threshold leading to comparatively large error in Douglas-Peucker vector data compression algorithm, which has difficulty in maintaining the uncertainty of shape features and threshold selection, a segmented Douglas-Peucker algorithm based on node importance is proposed. Firstly, the algorithm uses the vertical chord ratio as the main feature to detect and extract the critical points with large contribution to the shape of the curve, so as to ensure its basic shape. Then, combined with the radial distance constraint, it selects the maximum point as the critical point, and introduces the threshold related to the scale to merge and adjust the critical points, so as to realize local feature extraction between two critical points to meet the requirements in accuracy. Finally, through a large number of different vector data sets, the improved algorithm is analyzed and evaluated from qualitative and quantitative aspects. Experimental results indicate that the improved vector data compression algorithm is better than Douglas-Peucker algorithm in shape retention, compression error, results simplification and time efficiency.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.39-51
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• 2004

This study aims to suggest new algorithm, named as Simoo, in order to improve spatial conflicts and vector displacement between linear features in generalization of the linear features. Main principles of Simoo algorithm is adoption of simplification and smoothening methods. Tolerance conditions used in Simoo are perpendicular length, external angle, and average vertex length. Main characteristics of Simoo are the application of scale, cartographic refinement, minimization of logical errors, and maintenance of geographical properties. The Simoo was applied through comparison to existing Douglas-Peucker algorithm. Resultantly, maintenance ratios of line such as coastal line and stream network were over 97% in both algorithms. The elimination ratio of vertex points may be more effective in Douglas-Peucker than in Simoo. Spatial conflicts between linear features may be more minimized in Simoo. The curvature and smoothening of lines become decreased in scale in application of Simoo. Finally, Simoo algorithm may be more effective than Douglas-Peucker for cartographic generalization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.117-128
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• 1999

A Simple tree-structured line simplification method, which exactly follows the Douglas-Peucker algorithm, has a strength for its simplification index to be involved into the hierarchical data structures. However, the hierarchy of simplification index, which is the core in a simple tree method, may not be always guaranteed. It is validated that the local property of line features in such global approaches as Douglas-Peucker algorithm is apt to be neglected and the construction of hierarchy with no thought of locality may entangle the hierarchy. This study designed a new approach, CALS(Convex hull Applied Line Simplification), a) to search critical points of line feature with convex hull search technique, b) to construct the hierarchical data structure based on these critical points, c) to simplify the line feature using multiple trees. CALS improved the spatial accuracy as compared with a simple tree method. Especially CALS was excellent in case of line features having the great extent of sinuosity.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.298-311
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• 2015

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.360-363
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• 2015

In n-dimensional spatial data, Minimum Boundary Rectangle(MBR) was used to handle the moving object trajectories data. But, this method has inaccurate approximation. So, It makes many dead space and performs unnecessary operation when processing a query. In this paper, we offer new index structure using approximation. We developed algorithm that make index strucutre by using Douglas-Peucker Algorithm and had a comparison experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.465-472
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• 2015

This paper presents a visual touch recognition for NUI(Natural User Interface) using Voronoi-tessellation algorithm. The proposed algorithms are three parts as follows: hand region extraction, hand feature point extraction, visual-touch recognition. To improve the robustness of hand region extraction, we propose RGB/HSI color model, Canny edge detection algorithm, and use of spatial frequency information. In addition, to improve the accuracy of the recognition of hand feature point extraction, we propose the use of Douglas Peucker algorithm, Also, to recognize the visual touch, we propose the use of the Voronoi-tessellation algorithm. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.35-41
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• 2008

Recently needs for the convergence of CAD and GIS data are increasing, and many studies on converting two systems to each other are being carried out. In this study, to revise and update the building data of digital map using CAD data for construction, the outline of building is abstracted from the CAD data and the outline is generalized to the same level of detail with the building data of digital map. Several line simplification algorithms to generalize the outline are adopted and compared, especially at the view of satisfaction to the drawing rule for digital map. Douglas-Peucker algorithm, Lang's algorithm, Reumann-Witkam algorithm, and Opheim algorithm are applied as the line simplification method. To evaluate the results of these algorithms, visual assessment and variation ratio of the number of points, total length of lines, the area of polygon, and satisfaction ratio to the drawing rule of digital map are analyzed. The result of Lang algorithm and Douglas-Peucker algorithm show superior satisfaction ratio. But general satisfaction ratio is 50~60% for all algorithm. Therefore there seems to be a limit to use these algorithms for the simplification method to update the building data in digital map and it is necessary to develop line simplification algorithm which satisfy the drawing rule well.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.65-66
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• 2010

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.1
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• pp.77-83
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• 1995

This paper is concentrated on map generalization in digital environment for automated multi-scale map pro-duction using conventional hardcopy maps. Line generalization is urgently required process to prepare small scale digital map database when large scale map databases are available. This paper outlines a new approach to the line generalization when preparing small scale map on the basis of existing large scale distal map. Line generalizations are conducted based on zero-crossing algorithm using six sheets of 115,000 scale YEOSU area which produced by National Geographic Institute. The results are compared to Douglas-Peucker algorithm and manual method. The study gives full details of the data reduction rates and alternatives based on the proposed algorithm.