• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.639-646
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• 2003

Conventional two-tier databases have shown performance limitation in the presence of many concurrent clients. To this end, the three-tier architecture that exploits similarities in client's object access behavior has been proposed. In this system, clients are partitioned into clusters, and object requests can be then served in inter-cluster manner. Introducing an intermediate layer between server(s) and clients enables this. In this paper, we introduce the problem of client realignment in which access behavior changes, and propose on-line client clustering. This system facilitates adaptive reconfiguration and redistribution of sites. The core issue in this paper is to demonstrate the effectiveness of on-line client clustering. We experimentally investigate the performance of the scheme and necessary costs.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1115-1121
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• 2012

This paper proposes a method to extract accurate plane of an object in unstructured environment for a humanoid robot by using a laser scanner. By panning and tilting 2D laser scanner installed on the head of a humanoid robot, 3D depth map of unstructured environment is generated. After generating the 3D depth map around a robot, the proposed plane extraction method is applied to the 3D depth map. By using the hierarchical clustering method, points on the same plane are extracted from the point cloud in the 3D depth map. After segmenting the plane from the point cloud, dimensions of the planes are calculated. The accuracy of the extracted plane is evaluated with experimental results, which show the effectiveness of the proposed method to extract planes around a humanoid robot in unstructured environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.507-516
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• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.339-346
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• 2000

Medical image is analyzed to get an anatomical information for diagnostics. Segmentation must be preceded to recognize and determine the lesion more accurately. In this paper, we propose automatic segmentation algorithm for MR brain images using T1-weighted, T2-weighted and PD images complementarily. The proposed segmentation algorithm is first, extracts cerebrum images from 3 input images using cerebrum mask which is made from PD image. And next, find 3D clusters corresponded to cerebrum tissues using scale filtering and 3D clustering in 3D space which is consisted of T1, T2, and PD axis. Cerebrum images are segmented using FCM algorithm with its initial centroid as the 3D cluster's centroid. The proposed algorithm improved segmentation results using accurate cluster centroid as initial value of FCM algorithm and also can get better segmentation results using multi spectral analysis than single spectral analysis.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.277-282
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• 2003

The knowledge discovery from web has been studied in many researches. There are some difficulties using web log for training data on efficient information predictive models. In this paper, we studied on the method to eliminate sparseness from web log data and to perform web user clustering. Using missing value imputation by Bayesian inference of MCMC, the sparseness of web data is removed. And web user clustering is performed using self organizing maps based on 3-D plot by principal component. Finally, using KDD Cup data, our experimental results were shown the problem solving process and the performance evaluation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.70-77
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• 2004

In many industrial applications, an unexpected fault of induction motor drive systems can cause serious troubles such as downtime of the overall system heavy loss, and etc. As one of methods to solve such problems, this paper investigates the fault diagnosis for open-switch damages in a voltage-fed PWM inverter for induction motor drive. For the feature extraction of a fault we transform the current signals to the d-q axis and calculate mean current vectors. And then, for diagnosis of different fault patterns, we propose a clustering based diagnosis algorithm The proposed diagnostic technique is a modified ANFIS(Adaptive Neuro-Fuzzy Inference System) which uses a clustering method on the premise of general ANFIS's. Therefore, it has a small calculation and good performance. Finally, we implement the method for the diagnosis module of the inverter with MATLAB and show its usefulness.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1069-1072
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• 1987

In this paper, we drive useful data from 3-D depth information as input using discontinuity boundary or clustering. And using magnitude and direction of z-gradient we classify the data into adaptable primitive types through intrinsic and stochastical processing. After these processing information is reconstructed for forming data base. And make relationship and standard view position for matching.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.682-684
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• 2003

상호작용컴퓨터 그래픽스분야의 지배적인 역할을 하는 다각형모델의 간략화 표현을 위해서 본논문에서는 모델의 특징을 이용하고자한다. 본논문에서는 vertex clustring 알고리즘을 이용하여 다각형 메쉬모델을 간략화 한다. 이때 클러스터링하기 위한 셀의 크기를 결정하기 위하여 모델의 에지의 길이 특성을 이용하여 셀의 크기를 결정한다. 개선된 vertex clustering 방법은 기존의 방법에 비해 모델자체의 에지 특성을 이용하기 때문에 신뢰성있는 간략화를 수행할 수 있다.