• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.155-161
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• 2015

Dike lines are important features for describing the detailed shapes of dikes and for detecting topographic changes on dike surfaces. Historically, dike lines have been generated using only the LiDAR data. This paper proposes a new methodology for selecting an appropriate dike line on various dike surfaces using the topographic LiDAR data and multispectral orthoimages taken in the Nakdong River basins. The fi rst baselines were generated from the given LiDAR data using the modified convex hull algorithm and smoothing spline function, and the second baselines were generated from the given orthoimages by the Canny operator. Next, one baseline was selected among the two baselines at 10m intervals by comparing their elevations, and the selected baseline at 10m interval was defined as the dike line segment. Finally, the selected dike line segments were connected to construct the 3D dike lines. The statistical results show that the dike lines generated using both the LiDAR data and multispectral orthoimages had the improved horizontal and vertical accuracies than the dike lines generated only using the LiDAR data on the various dike surfaces.

• Korean Journal of Environment and Ecology
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• v.27 no.1
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• pp.11-21
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• 2013

We tried to find out the core feeding site of the Red-crowned Crane(Grus japonensis) in Cheorwon, Korea by using analysis techniques which are MCP(minimum convex polygon), KDE(kernel density estimation), LoCoH(local nearest-neighbor convex-hull). And, We discussed the difference and meaning of result among analysis methods. We choose the data of utilization distribution from distribution map of Red-crowned Crane in Cheorwon, Korea at $17^{th}$ February 2012. Extent of the distribution area was $140km^2$ by MCP analysis. Extents of core feeding area of the Red-crowned Crane were $33.3km^2$($KDE_{1000m}$), $25.7km^2$($KDE_{CVh}$), $19.7km^2$($KDE_{LSCVh}$), according to the 1000m, CVh, LSCVh in value of bandwidth. Extent, number and shape complexity of the core area has decreased, and size of each core area have decreased as small as the bandwidth size(default:1000m, CVh: 554.6m, LSCVh: 329.9). We would suggest the CVh value in KDE analysis as a proper bandwidth value for the Red-crowned crane's core area zoning. Extent of the distribution range and core area have increased and merged into the large core area as a increasing of k value in LoCoH analysis. Proper value for the selecting core area of Red-crowned Crane's distribution was k=24, and extent of the core area was $18.2km^2$, 16.5% area of total distribution area. Finally, the result of LoCoH analysis, we selected two core area, and number of selected core area was smaller than selected area of KDE analysis. Exact value of bandwidth have not been used in studies using KDE analysis in most articles and presentations of the Korea. As a result, it is needed to clarify the exact using bandwidth value in KDE studies.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.1-9
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• 2016

Linear interpolation is a basic sampling method for volume visualization. This method generates good images but sometimes it is inferior to our high expectation because it is encouraged to produce high quality images in the medical applications. In this paper, B spline based tri-cubic interpolation is used for the re-sampling step. The conventional B spline is an approximation method which does not cross control points so that we moved the control points and the curve crosses the original control points. In the rendering step, the empty space leaping is applicable to increase rendering speed. We have to calculate the maximum and minimum values for each block to detect empty space. The convex hull property of B spline enables the values of control points to be used as the maximum and minimum values. As a result, tri-cubic interpolated volume rendering is possible in interactive speed.

• Journal of the Korea Convergence Society
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• v.6 no.3
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• pp.51-57
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• 2015

This paper proposes an efficient method such as Extended Three Region Partitioning Method for nested loops with irregular dependences for maximizing parallelism. Our approach is based on the Convex Hull theory, and also based on minimum dependence distance tiling, the unique set oriented partitioning, and three region partitioning methods. In the proposed method, we eliminate anti dependences from the nested loop by variable renaming. After variable renaming, we present algorithm to select one or more appropriate lines among given four lines such as LMLH, RMLH, LMLT and RMLT. If only one line is selected, the method divides the iteration space into two parallel regions by the selected line. Otherwise, we present another algorithm to find a serial region. The selected lines divide the iteration space into two parallel regions as large as possible and one or less serial region as small as possible. Our proposed method gives much better speedup and extracts more parallelism than other existing three region partitioning methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.85-86
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• 2012

e-Navigation strategy IMO promotes is defined as it is necessary to network to provide various maritime safety information to in land and on board users, and it is expected to provide a large amount and diverse kinds of maritime spatial information services to them frequently. However, as there are some limits to transmit that by current mobile maritime communication technologies, it is required to simplify and optimize the information. In this study, tree node and convex hull method is applied to S-100 SD spatial information to generalize and we arranged the efficiency and effect of generalization by storing in XML form which can be used in general.

• The KIPS Transactions:PartA
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• v.14A no.7
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• pp.435-442
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• 2007

This paper is concerned with the mesh segmentation problem that can be applied to diverse applications such as texture mapping, simplification, morphing, compression, and shape matching for 3D mesh models. The mesh segmentation is the process of dividing a given mesh into the disjoint set of sub-meshes. We propose a method for segmenting meshes by simultaneously reflecting global and local geometric characteristics of the meshes. First, we extract sharp vertices over mesh vertices by interpreting the curvatures and convexity of a given mesh, which are respectively contained in the local and global geometric characteristics of the mesh. Next, we partition the sharp vertices into the $\kappa$ number of clusters by adopting the $\kappa$-means clustering method [29] based on the Euclidean distances between all pairs of the sharp vertices. Other vertices excluding the sharp vertices are merged into the nearest clusters by Euclidean distances. Also we implement the proposed method and visualize its experimental results on several 3D mesh models.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.939-955
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• 2010

With the progress of IT and mobile positioning technologies, various types of location-based services (LBS) have been proposed and implemented. Finding a shortest path between two nodes is one of the most fundamental tasks in many LBS related applications. So far, there have been many research efforts on the shortest path finding problem. For instance, $A^*$ algorithm estimates neighboring nodes using a heuristic function and selects minimum cost node as the closest one to the destination. Pruning method, which is known to outperform the A* algorithm, improves its routing performance by avoiding unnecessary exploration in the search space. For pruning, shortest paths for all node pairs in a map need to be pre-computed, from which a shortest path container is generated for each edge. The container for an edge consists of all the destination nodes whose shortest path passes through the edge and possibly some unnecessary nodes. These containers are used during routing to prune unnecessary node visits. However, this method shows poor performance as the number of unnecessary nodes included in the container increases. In this paper, we focus on this problem and propose a new border line-based pruning scheme for path routing which can reduce the number of unnecessary node visits significantly. Through extensive experiments on randomly-generated, various complexity of maps, we empirically find out optimal number of border lines for clipping containers and compare its performance with other methods.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.67-76
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• 2012

Traffic classification plays an important role in traffic management. To traditional methods, P2P and encryption traffic may become a problem. Support Vector Machine (SVM) is a useful classification tool which is able to overcome the traditional bottleneck. The main disadvantage of SVM algorithms is that it's time-consuming to train large data set because of the quadratic programming (QP) problem. However, the useful support vectors are only a small part of the whole data. If we can discard the useless vectors before training, we are able to save time and keep accuracy. In this article, we discussed the feasibility to remove the useless vectors through a sequential method to accelerate training speed when dealing with large scale data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2761-2768
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• 2010

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by projection runlength, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our feature sets was tested by recognition experiments on the handwritten numeral database CENPARMI, where we used SVM with RBF kernel as a classifier. The experimental results showed that each combination of two or three features gave a better performance than a single feature. This means that each single feature works with a different discriminating power and cooperates with other features to enhance the recognition accuracy. By using the composite feature of the three features, we achieved a recognition rate of 98.90%.