• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.53-61
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• 2006

In this paper, we propose a novel method for 3D reconstruction of an indoor scene using a multi-view camera. Until now, numerous disparity estimation algorithms have been developed with their own pros and cons. Thus, we may be given various sorts of depth images. In this paper, we deal with the generation of a 3D surface using several 3D point clouds acquired from a generic multi-view camera. Firstly, a 3D point cloud is estimated based on spatio-temporal property of several 3D point clouds. Secondly, the evaluated 3D point clouds, acquired from two viewpoints, are projected onto the same image plane to find correspondences, and registration is conducted through minimizing errors. Finally, a surface is created by fine-tuning 3D coordinates of point clouds, acquired from several viewpoints. The proposed method reduces the computational complexity by searching for corresponding points in 2D image plane, and is carried out effectively even if the precision of 3D point cloud is relatively low by exploiting the correlation with the neighborhood. Furthermore, it is possible to reconstruct an indoor environment by depth and color images on several position by using the multi-view camera. The reconstructed model can be adopted for interaction with as well as navigation in a virtual environment, and Mediated Reality (MR) applications.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.551-566
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• 2016

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. Especially, the degradation gives bad influence in the analysis of images collected over the scene with complicate surface structure such as urban area. This study proposes a multi-stage image restoration to improve the accuracy of detailed analysis for the images collected over the complicate scene. The proposed method assumes a Gaussian additive noise, Markov random field of spatial continuity, and blurring proportional to the distance between the pixels. Point-Jacobian Iteration Maximum A Posteriori (PJI-MAP) estimation is employed to restore a degraded image. The multi-stage process includes the image segmentation performing region merging after pixel-linking. A dissimilarity coefficient combining homogeneity and contrast is proposed for image segmentation. In this study, the proposed method was quantitatively evaluated using simulation data and was also applied to the two panchromatic images of super-high resolution: Dubaisat-2 data of 1m resolution from LA, USA and KOMPSAT3 data of 0.7 m resolution from Daejeon in the Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution panchromatic imagery.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of Plant Biotechnology
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• v.37 no.1
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• pp.12-24
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• 2010

Plant metabolomics is a research field for identifying all of the metabolites found in a certain plant cell, tissue, organ, or whole plant in a given time and conditions and for studying changes in metabolic profiling as time goes or conditions change. Metabolomics is one of the most recently developed omics for holistic approach to biology and is a kind of systems biology. Metabolomics or metabolite fingerprinting techniques usually involves collecting spectra of crude solvent extracts without purification and separation of pure compounds or not in standardized conditions. Therefore, that requires a high degree of reproducibility, which can be achieved by using a standardized method for sample preparation and data acquisition and analysis. In plant biology, metabolomics is applied for various research fields including rapid discrimination between plant species, cultivar and GM plants, metabolic evaluation of commercial food stocks and medicinal herbs, understanding various physiological, stress responses, and determination of gene functions. Recently, plant metabolomics is applied for characterization of gene function often in combination with transcriptomics by analyzing tagged mutants of the model plants of Arabidopsis and rice. The use of plant metabolomics combined by transcriptomics in functional genomics will be the challenge for the coming year. This review paper attempted to introduce current status and prospects of plant metabolomics research.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.29-32
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• 2005

3차원 비디오는 차세대 정보 통신 서비스 분야의 하나로, 사용자에게 시각적으로 고차원적인 서비스를 제공하는 것을 목적으로 한다. 이 가운데 다시점 비디오는 같은 시간, 여러 시점에서 영상 정보를 획득하여 사용자에게 원하는 시점의 영상 정보를 제공하는 3차원 비디오이며, 현재 방송 관련 연구 기관에서 차세대 실감방송 멀티미디어 서비스 개발을 목적으로 하는 연구가 활발히 진행되고 있다. 최근 MPEG 표준화 그룹에서는 다시점 비디오 부호화 (multi-view video coding, MVC) 방법에 관한 표준화 작업이 진행 중이며, 최신 비디오 압축 표준인 H.264를 이용한 여러 가지 방법들이 제안되었다. 현재 MVC 표준화 작업의 평가 기준이 되는 방법은 각 시점을 H.264로 부호화하는 방법인데, 이는 다시점 비디오 영상의 중요한 특성인 인접시점들 사이의 공간적 상관도를 전혀 고려하지 않았다. 본 논문에서는 시점 보간법을 이용하여 얻어진 중간 영상과 원영상과의 차분 영상을 부호화하는 알고리즘을 제안하고자 한다. 여기서 시점 보간법이란 좌우 두 시점 영상으로부터 변이값을 얻은 다음, 이를 이용하여 중간 시점 영상을 합성하는 방법을 말한다. 예를 들면,다시점 비디오의 홀수 번째 시점의 영상은 기존의 방법을 따르고, 짝수 번째 시점의 영상은 이미 부호화된 홀수 번째 시점의 영상을 이용하여 보간적으로 예측하여 원래 영상과 차분 영상을 구하여 부호화한다. 차분 영상은 영상의 복잡도가 많이 감소되어 원영상에 비해 보다 나은 부호화 효율을 보인다. 그러나 합성 영상이 각 장면마다 독립적으로 생성되므로 원영상에 비해 차분 영상의 시간적인 상관도가 줄어들어 I장면의 경우 부호화 효율이 크게 향상되었으나, 시간적인 상관도를 이용하는 P장면과 B장면에서는 오히려 좋지 않은 결과를 보였다. 통계는 전 국민에 대한 패널자료이기 때문에 통계적 활용의 범위가 방대하다. 특히 개인, 가구, 사업체 등 사회 활동의 주체들이 어떻게 변화하는지를 추적할 수 있는 자료를 생산함으로써 다양한 인과적 통계분석을 할 수 있다. 행정자료를 활용한 인구센서스의 이러한 특징은 국가의 교육정책, 노동정책, 복지정책 등 다양한 정책을 정확한 자료를 근거로 수립할 수 있는 기반을 제공한다(Gaasemyr, 1999). 이와 더불어 행정자료 기반의 인구센서스는 비용이 적게 드는 장점이 있다. 예를 들어 덴마크나 핀란드에서는 조사로 자료를 생산하던 때의 1/20 정도 비용으로 행정자료로 인구센서스의 모든 자료를 생산하고 있다. 특히, 최근 모든 행정자료들이 정보통신기술에 의해 데이터베이스 형태로 바뀌고, 인터넷을 근간으로 한 컴퓨터네트워크가 발달함에 따라 각 부처별로 행정을 위해 축적한 자료를 정보통신기술로 연계${cdot}$통합하면 막대한 조사비용을 들이지 않더라도 인구센서스자료를 적은 비용으로 생산할 수 있는 근간이 마련되었다. 이렇듯 행정자료 기반의 인구센서스가 많은 장점을 가졌지만, 그렇다고 모든 국가가 당장 행정자료로 인구센서스를 대체할 수 있는 것은 아니다. 행정자료로 인구센서스통계를 생산하기 위해서는 각 행정부서별로 사용하는 행정자료들을 연계${cdot}$통합할 수 있도록 국가사회전반에 걸쳐 행정 체제가 갖추어져야 하기 때문이다. 특히 모든 국민 개개인에 관한 기본정보, 개인들이 거주하며 생활하는 단위인 개별 주거단위에 관한 정보가 행정부에 등록되어 있고, 잘 정비되어 있어야 하며, 정보의 형태 또한 서로 연계가 가능하도록 표준화되어있어야 한다. 이와 더불어, 현재 인구센서스에서 표본조사를 통해 부가적으로 생산하는 경제활동통계를 생산하기 위해서는 개인이

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.189-213
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• 2017

This study seeks to critically examine the significance and limits of the cluster adaptive cycle model for analysis of cluster evolution and to propose research issues for future analysis of cluster evolution based on this critical examination. Until the 1980s, research on industrial complexes including clusters was based on a 'static perspective' that focuses on the aspect of economic space at a specific point in time, but the research paradigm has recently shifted to a 'dynamic perspective' focusing on 'evolution' of 'complex adaptive systems'. As a result, the adaptive cycle model has attracted attention as an analysis tool of dynamically evolving clusters. However, the cluster adaptive cycle model has emerged by being appropriately modified and expanded according to the properties of the cluster and its evolution. The cluster adaptive cycle model is a comprehensive analysis framework that identifies the characteristics of cluster evolution in terms of resource accumulation, interdependence, and resilience and classifies cluster evolution paths into six different categories. Nevertheless, there is still a need for further discussion and supplementation in terms of theoretical and empirical research to expand and deepen the model. Therefore, research issues for future analysis of cluster evolution are to specify and elaborate the cluster evolution model, to emphasize the concept of resilience, and to verify the applicability and usefulness of the model through empirical research.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.85-94
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• 2005

Display characterization, deriving the relationship between digital input values and the corresponding CIEXYZ tri-stimulus values, is important to reproduce the accurate color in color management system. The relationship can be estimated from the nine channel TRCs(tone response curves) and the result of this characterization method is better than that of using three channel TRCs. However, the inverse display characterization using nine channel TRCs cannot be directly inverted because the CIEXYZ values corresponding to each of RGB values are inseparable. Accordingly, inverse display characterization is usually implemented by the 3D-LUT (look-up table) method. Although the result of 3B-LUT is accurate, creating the 3D-LUT requires a lot of memory space and considerable amount of measurements. Therefore the inverse characterization method is proposed based on the modeling of channel-dependent values and nine channel inverse process based on the GOG(gain, offset gamma) model. The proposed method enhances the accuracy of display characterization and reduces the complexity and the number of measurements data required for accuracy in 3-D LUT.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.39-52
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• 2005

In this paper, a registration method is presented to register partial 3D point clouds, acquired from a multi-view camera, for 3D reconstruction of an indoor environment. In general, conventional registration methods require a high computational complexity and much time for registration. Moreover, these methods are not robust for 3D point cloud which has comparatively low precision. To overcome these drawbacks, a projection-based registration method is proposed. First, depth images are refined based on temporal property by excluding 3D points with a large variation, and spatial property by filling up holes referring neighboring 3D points. Second, 3D point clouds acquired from two views are projected onto the same image plane, and two-step integer mapping is applied to enable modified KLT (Kanade-Lucas-Tomasi) to find correspondences. Then, fine registration is carried out through minimizing distance errors based on adaptive search range. Finally, we calculate a final color referring colors of corresponding points and reconstruct an indoor environment by applying the above procedure to consecutive scenes. The proposed method not only reduces computational complexity by searching for correspondences on a 2D image plane, but also enables effective registration even for 3D points which have low precision. Furthermore, only a few color and depth images are needed to reconstruct an indoor environment.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.8
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• pp.619-630
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• 2003

We present a method of runtime configuration scheduling in reconfigurable SoC design. As a model of computation, we use a popular formal model of computation, hierarchical FSM (HFSM) with synchronous dataflow (SDF) model, in short, HFSM-SDF model. In reconfigurable SoC design with HFSM-SDF model, the problem of configuration scheduling becomes challenging due to the dynamic behavior of the system such as concurrent execution of state transitions (by AND relation), complex control flow (HFSM), and complex schedules of SDF actor firing. This makes it hard to hide configuration latency efficiently with compile-time static configuration scheduling. To resolve the problem, it is necessary to know the exact order of required configurations during runtime and to perform runtime configuration scheduling. To obtain the exact order of configurations, we exploit the inherent property of HFSM-SDF that the execution order of SDF actors can be determined before executing the state transition of top FSM. After obtaining the order information and storing it in the ready configuration queue (ready CQ), we execute the state transition. During the execution, whenever there is FPGA resource available, a new configuration is selected from the ready CQ and fetched by the runtime configuration scheduler. We applied the method to an MPEG4 decoder and IS95 design and obtained up to 21.8% improvement in system runtime with a negligible overhead of memory usage.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1085-1095
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• 2019

In this paper, we perform a subjective quality assessment on low-resolution surveillance videos, which are encoded with a very low target bit-rate to use in an ultra-low band transmission system and investigate the encoding effects on the perceived video quality. The test videos are collected based on their spatial and temporal characteristics which affect the perceived quality. H.265/HEVC encoder is used to prepare the impaired sequences for three target bit-rates 20, 45, and 65 kbps and subjective quality assessment is conducted to evaluate the quality from a viewing distance of 3H. The experimental results show that the quality of encoded videos, even at target bit-rate of 45 kbps can satisfy the users. Also we compare objective image/video quality assessment methods on the proposed dataset to measure their correlation with subjective scores. The experimental results show that the existing methods poorly performed, that indicates the need for a better quality assessment method.