• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.119-124
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• 2000

One of the main problems in 3D shape measuring systems that use the triangulation of line-shaped laser light is precise center line detection of line-shaped laser stripe. The intensity of a line-shaped laser light stripe on the CCD image varies following to the reflection angles, colors and shapes of objects. In this paper, a new center line detection algorithm to compensate the local intensity variation on a line-shaped laser light stripe is proposed. The 3-D surface shape measuring system using the proposed center line detection algorithm can measure 3-D surface shape with enhanced measurement resolution by using the dynamic shape reconstruction with adaptive pattern clustering of the line-shaped laser light. This proposed 3-D shape measuring system can be easily applied to practical situations of measuring 3-D surface by virtue of high speed measurement and compact hardware compositions.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.889-894
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• 2004

The fuzzy kernel c-means (FKCM) algorithm, which uses a kernel function, can obtain more desirable clustering results than fuzzy c-means (FCM) for not only spherical data but also non-spherical data. However, it can be sensitive to noise as in the FCM algorithm. In this paper, a kernel function is applied to the possibilistic c-means (PCM) algorithm and is shown to be robust for data with additive noise. Several experimental results show that the proposed kernel possibilistic c-means (KPCM) algorithm out performs the FKCM algorithm for general data with additive noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.190-207
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• 2015

In the combustion engineering, to decrease pollution and increase production efficiency, and to optimally keep solid burning material amount constant in a burner online, it needs a smart method to detect the amount variation of the burning materials in a high temperature environment. This paper presents an online machine vision system for automatically measuring and detecting the burning material amount inside a burner or a boiler. In the camera-protecting box of the system, a sub-system for cooling is constructed by using the cooling water circulation techqique. In addition, the key and intelligent step in the system is to detect the pile profile of the variable burning material, and the algorithm for the pile profile tracing was studied based on the combination of the gey level (color) discontinuity and similarity based image segmentation methods, the discontinuity based sub-algorithm is made on the quaternion convolution, and the similarity based sub-algorithm is designed according to the region growing with multi-scale clustering. The results of the two sub-algoritms are fused to delineate the final pile profile, and the algorithm has been tested and applied in different industrial burners and boilers. The experiements show that the proposed algorithm works satisfactorily.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.467-472
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• 2005

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.41-47
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• 2002

Fractal image compression can reduce the size of image data by the contractive mapping that is affine transformation to find the block(called as range block) which is the most similar to the original image. Even though fractal image compression is regarded as an efficient way to reduce the data size, it has high distortion rate and requires long encoding time. In this paper, we presented a hybrid fractal image compression system with the modified SOFM Vector Quantizer which uses improved competitive learning method. The simulation results showed that the VQ hybrid fractal using improved competitive loaming SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.196-201
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• 2007

The Fuzzy E-means (FCM) algorithm is a widely used clustering method that incorporates probabilitic memberships. Due to these memberships, it can be sensitive to noise data. In this paper, we propose a new fuzzy C-means clustering algorithm by incorporating the Parzen Window method to include density information of the data. Several experimental results show that our proposed density-based FCM algorithm outperforms conventional FCM especially for data with noise and it is not sensitive to initial cluster centers.

• Journal of KIISE:Software and Applications
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• v.27 no.10
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• pp.1038-1045
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• 2000

본 논문에서는 칼라공간상의 거리와 이웃정보를 이용한 클러스터링을 통한 칼라영상 분할 방법을 제안한다. 칼라영상의 한 픽셀은 칼라정보(R.G.B)와 위치정보(x.y)를 가진다. 대개의 칼라공간에서의 클러스터링방법은 픽셀을 (R,G,B)공간으로 변환후 (R,G,B)공간상의 분포만을 이용하지만 여기서는(R,G,B)와 (x.y)모두를 사용하여 클러스터링함으로 영상의 세그먼트들을 찾는다. 클러스터링 방법으로서 인력을 모방하는 중력 클러스터링(gravitational clustering)을 사용하였다. 이 방법은 클러스터의 중심값과 클러스터 수를 미리 정해주지 않아도 자동적으로 결정할 수 있는 장점이 있다. 중력 클러스터링에서 찾은 클러스터 수를 가지고 다른 클러스터링 방법(K-means)에 입력으로 주어 결과를 비교해 본다. 본 논문에서는 이웃관계를 따라 클러스터링하는 것이 정확한 경계선을 찾는데 효과적임을 보여준다.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.109-120
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• 2005

This landscape design proposal was presented to a design competition for Osong Biohealth Technopolis Institute of Cheongwon Gun Chung Cheong Buk Do which was held by Ministry of Health and Welfare in March 2004. The site is located in. Osong Li, Kang Wei Myun, Cheonwon Gun, Chung Cheong Buk Do and has an area of $402,600m^2$. The judging criteria for landscape design set by the client could be articulated as follows: an environment friendly design respecting the surrounding environment, a functionally efficient site plan by clustering buildings with similar uses, a site plan having 'front yard' by locating buildings in rear areas toward existing 'groves'. The proposal set the main design concept of this project as 'clustering'. By doing that, existing grades and plants can be saved, buildings with similar uses can be clustered, huge 'front yard' as a symbolic image of this project can be achieved, and finally many small open spaces for everyday life can be designed accordingly.