• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.529-542
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• 2000

Recent years, the use of multimedia information is rapidly increasing, and the video media is the most rising one than any others, and this field Integrates all the media into a single data stream. Though the availability of digital video is raised largely, it is very difficult for users to make the effective video access, due to its length and unstructured video format. Thus, the minimal interaction of users and the explicit definition of video structure is a key requirement in the lately developing image and video management systems. This paper defines the terms and hierarchical video structure, and presents the system, which construct the clustering-based video hierarchy, which facilitate users by browsing the summary and do a random access to the video content. Instead of using a single feature and domain-specific thresholds, we use multiple features that have complementary relationship for each other and clustering-based methods that use normalization so as to interact with users minimally. The stage of shot boundary detection extracts multiple features, performs the adaptive filtering process for each features to enhance the performance by eliminating the false factors, and does k-means clustering with two classes. The shot list of a result after the proposed procedure is represented as the video hierarchy by the intelligent unsupervised clustering technique. We experimented the static and the dynamic movie videos that represent characteristics of various video types. In the result of shot boundary detection, we had almost more than 95% good performance, and had also rood result in the video hierarchy.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.411-418
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• 2009

Vector quantization algorithm based on fuzzy clustering has been widely used in the field of data compression since the use of fuzzy clustering analysis in the early stages of a vector quantization process can make this process less sensitive to its initialization. However, the process of fuzzy clustering is computationally very intensive because of its complex framework for the quantitative formulation of the uncertainty involved in the training vector space. To overcome the computational burden of the process, this paper introduces an array architecture for the implementation of fuzzy vector quantization (FVQ). The arrayarchitecture, which consists of 4,096 processing elements (PEs), provides a computationally efficient solution by employing an effective vector assignment strategy during the clustering process. Experimental results indicatethat the proposed parallel implementation providessignificantly greater performance and efficiency than appropriately scaled alternative array systems. In addition, the proposed parallel implementation provides 1000x greater performance and 100x higher energy efficiency than other implementations using today's ARMand TI DSP processors in the same 130nm technology. These results demonstrate that the proposed parallel implementation shows the potential for improved performance and energy efficiency.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.386-389
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• 1999

In this study, visible and near infrared channels of NOAA/AVHRR data were used to classify land use and vegetation types over Korean peninsula. Analyzing forest stand structures and prediction of forest productivity using satellite data were also reviewed. Land use and land cover classification was made by unsupervised clustering methods. After monthly Normalized Difference Vegetation Index (NDVI) composite images were derived from April to November 1998, the derived composite images were used as temporal feature vector's in this clustering analysis. Visually interpreted, the classification result was satisfactory in overall for it matched well with the general land cover patterns. But subclassification of forests into coniferous, deciduous, and mixed forests were much confused due to the effects of low ground resolution of AVHRR data and without defined classification scheme. To investigate into the forest stand structures, digital forest type maps were used as an ancillary data. Forest type maps, which were compiled and digitalized by Forestry Research Institute, were registered to AVHRR image coordinates. Two data sets were compared and percent forest cover over whole region was estimated by multiple regression analysis. Using this method, other forest stand structure characteristics within the primary data pixels are expected to be extracted and estimated.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1147-1161
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• 2015

Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.37-38
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• 2011

본 논문은 상대적으로 대비도 차이가 크게 나타나는 역광 이미지에 대해서 Retinex 알고리즘을 적용하여 보정 했을 경우 발생하는 밝은 영역에서의 컬러 성분의 손실을 개선하기 위한 새로운 기법을 제안한다. 역광 이미지의 경우 밝은 영역과 어두운 영역에 대한 밝기 차이가 매우 크게 발생하기 때문에 Retinex 알고리즘을 이용하여 영상의 대비도를 향상시킬 경우 밝은 영역에서의 컬러 성분이 손실되는 현상이 발생한다. 이러한 손실을 보완하기 위해서 원본 영상의 밝은 영역에 해당하는 컬러 성분을 Retinex 알고리즘으로 보정된 영상에 추가해준다. Fuzzy c-means 군집화 알고리즘을 이용하여 원본 영상에서의 밝은 영역과 어두운 영역에 대하여 모든 화소의 소속 정도를 나타내는 퍼지 소속 함수를 구한다. 밝은 영역에 대해서의 컬러 성분은 원본 영상 값에 밝은 영역 퍼지 소속 함수를 적용하고, 어두운 영역에 대해서의 컬러 성분은 Retinex 복원 영상 값에 어두운 영역 퍼지 소속 함수를 이용한다. 제안하는 알고리즘의 성능 평가를 위해 역광 현상이 강하게 나타나는 자연영상들을 대상으로 적용하여 기존의 Retinex 알고리즘(MSRCR) 보다 우수한 성능을 가지고 있음을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.712-718
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• 2003

C.elegans are often used to study of function of gene, but it is difficult for human observation to distinguish the mutants of C.elegans. To solve this problem, the system, which can classify the mutant types automatically using the computer vision, is now studying. Tn previous work［1］, we described the preprocessing method for automated-classification system. In this paper, we introduce shape features, which can be extracted from an acquisition image. We divide the feature into two categories, which are related to size and posture of the worm, and each feature is described mathematically We validate the shape information experimentally. And we use hierarchical clustering algorithm for classification. It reveals that 4 mutants of the worm, which are used in experiment, can be classified with over 90% of success rate.

• Journal of Navigation and Port Research
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• v.35 no.10
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• pp.793-798
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• 2011

ENCs are encoded using a numerical charts developed for publishing paper charts and serviced in forms of grid styles. For this reason, the density of ENCs' sounding information is not consistent and that requires improved methods. In this study, K-Means, ISODATA clustering algorithm as classification methods for satellite image was reviewed and adopted to this case study. The designed algorithm includes loading module for ENC data, improvement algorithm of sounding information, writing module of ENC data. According to the results of algorithm, we could confirm the improved result.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.670-674
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• 2018

Social network service (SNS) related data including comments/text, images, videos, blogs, and user experiences contain a wealth of information which can be used to build recommendation systems for various clients' and provide insightful data/results to business analysts. Multimedia data, especially visual data like image and videos are the richest source of SNS data which can reflect particular region, and cultures values/interests, form a gigantic portion of the overall data. Mining such huge amounts of data for extracting actionable intelligence require efficient and smart data analysis methods. The purpose of this paper is to focus on this particular modality for devising ways to model, index, and retrieve data as and when desired.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.255-263
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• 2022

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.