• Title, Summary, Keyword: 영상처리

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Interactivity within large-scale brain network recruited for retrieval of temporally organized events (시간적 일화기억인출에 관여하는 뇌기능연결성 연구)

  • Nah, Yoonjin;Lee, Jonghyun;Han, Sanghoon
    • Korean Journal of Cognitive Science
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    • v.29 no.3
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    • pp.161-192
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    • 2018
  • Retrieving temporal information of encoded events is one of the core control processes in episodic memory. Despite much prior neuroimaging research on episodic retrieval, little is known about how large-scale connectivity patterns are involved in the retrieval of sequentially organized episodes. Task-related functional connectivity multivariate pattern analysis was used to distinguish the different sequential retrieval. In this study, participants performed temporal episodic memory tasks in which they were required to retrieve the encoded items in either the forward or backward direction. While separately parsed local networks did not yield substantial efficiency in classification performance, the large-scale patterns of interactivity across the cortical and sub-cortical brain regions implicated in both the cognitive control of memory and goal-directed cognitive processes encompassing lateral and medial prefrontal regions, inferior parietal lobules, middle temporal gyrus, and caudate yielded high discriminative power in classification of temporal retrieval processes. These findings demonstrate that mnemonic control processes across cortical and subcortical regions are recruited to re-experience temporally-linked series of memoranda in episodic memory and are mirrored in the qualitatively distinct global network patterns of functional connectivity.

Design and Implementation of OpenCV-based Inventory Management System to build Small and Medium Enterprise Smart Factory (중소기업 스마트공장 구축을 위한 OpenCV 기반 재고관리 시스템의 설계 및 구현)

  • Jang, Su-Hwan;Jeong, Jopil
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.1
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    • pp.161-170
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    • 2019
  • Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

Development of Linking & Management System for High-Resolution Raw Geo-spatial Data based on the Point Cloud DB (Point Cloud 기반의 고해상도 원시데이터 연계 및 관리시스템 개발)

  • KIM, Jae-Hak;LEE, Dong-Ha
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.4
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    • pp.132-144
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    • 2018
  • 3D Geo-spatial information models have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, in surveying and geo-spatial field, the demand for high quality 3D geospatial information and indoor spatial information is so highly increasing. However, it is so difficult to provide a low-cost and high efficiency service to the field which demand the highest quality of 3D model, because pre-constructed spatial data are composed of different formats and storage structures according to the application purpose of each institutes. In fact, the techniques to construct a high applicable 3D geo-spatial model is very expensive to collect and analyze geo-spatial data, but most demanders of 3D geo-spatial model never want to pay the high-cost to that. This study, therefore, suggest the effective way to construct 3D geo-spatial model with low-cost of construction. In general, the effective way to reduce the cost of constructing 3D geo-spatial model as presented in previous studies is to combine the raw data obtained from point cloud observatory and UAV imagery, however this method has some limitation of usage from difficulties to approve the use of raw data because of those have been managed separately by various institutes. To solve this problem, we developed the linking & management system for unifying a high-Resolution raw geo-spatial data based on the point cloud DB and apply this system to extract the basic database from 3D geo-spatial mode for the road database registration. As a result of this study, it can be provided six contents of main entries for road registration by applying the developed system based on the point cloud DB.

A Study on Immersive Interaction Between HMD User and Non-HMD User for Presence of Asymmetric Virtual Reality (비대칭 가상현실에서의 현존감을 위한 HMD 사용자와 Non-HMD 사용자간 몰입형 상호작용에 관한 연구)

  • Lee, Jiwon;Kim, Mingyu;Kim, Jinmo
    • Journal of the Korea Computer Graphics Society
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    • v.24 no.3
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    • pp.1-10
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    • 2018
  • This study proposes an immersive interaction optimized for the user's experience environment to provide an improved presence for both HMD and Non-HMD users in the asymmetric virtual reality (VR) environment. The core of the proposed immersive interaction is to distinguish the differences of the asymmetric environment between the HMD and Non-HMD users and present the optimized interaction to the user. And, in order to increase the presence by providing improved immersion in the asymmetric virtual reality environment given to each user, we design the walking interaction to improve the immersion of space for the HMD users, a hand-based interface that improves immersion by fully understanding and managing the situation through direct control. Finally, through the experiment using questionnaire, it is verified that the immersive interaction provides all users with an enhanced presence and specialized experience in each environment experience. Through these processes, we confirmed that the Non-HMD user can be immersed in an asymmetric virtual reality using by proposed interaction as participant rather than assistant with HMD user.

Resting-State Electroencephalography (EEG) Functional Connectivity Analysis (안정기 뇌파를 이용한 기능적 연결성 분석)

  • Kim, Hunmin;Hwang, Hee
    • Journal of the Korean Child Neurology Society
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    • v.26 no.3
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    • pp.129-134
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    • 2018
  • Advances in network science and computer engineering have enabled brain connectivity analysis using clinical big data such as brain magnetic resonance imaging (MRI), electroencephalography (EEG), or magnetoencephalography (MEG). Resting-state functional connectivity analysis aims to reveal the characteristics of functional brain network in various diseases and normal brain maturation using resting-state EEG. Simplified sequence of resting-state functional connectivity analysis methods will be reviewed in this article. The outcomes from EEG resting-state connectivity analysis are comprised of connectivity itself of the specific condition and the network topology measure which describe the characteristics of specific connectivity. An increasing number of studies report the differences in the functional connection itself, global network measures including segregation (connectedness), integration (efficiency), and importance of specific nodes (centrality or node degree). Several issues that are relevant in the resting-state connectivity analysis are obtaining good quality EEG for analysis, consideration of particular features of EEG signal, understanding different types of association measures, and statistics for comparison of connectivities. Well-designed and carefully analyzed EEG resting-state connectivity analysis can provide useful information for patient care in pediatric neurology.

Enhancement of Ionospheric Correction Method Based on Multiple Aperture Interferometry (멀티간섭기법에 기반한 이온왜곡 보정기법의 보완)

  • Lee, Won-Jin;Jung, Hyung-Sup;Chae, Sung-Ho;Baek, Wonkyung
    • Korean Journal of Remote Sensing
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    • v.31 no.2
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    • pp.101-110
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    • 2015
  • Synthetic Aperture Radar Interferometry (InSAR) is affected by various noise source such as atmospheric artifact, orbital error, processing noise etc.. Especially, one of the dominant noise source for long-wave SAR system, such as ALOS PALSAR (L-band SAR satellite) is the ionosphere effect because phase delays on radar pulse through the ionosphere are proportional to the radar wavelength. To avoid misinterpret of phase signal in the interferogram, it is necessary to detect and correct ionospheric errors. Recently, a MAI (Multipler Aperture SAR Interferometry) based ionospheric correction method has been proposed and considered one of the effective method to reduce phase errors by ionospheric effect. In this paper, we introduce the MAI-based method for ionospheric correction. Moreover we propose an efficient method that apply the method over non-coherent area using directional filter. Finally, we apply the proposed method to the ALOS PALSAR pairs, which include the west sea coast region in Korea. A polynomial fitting method, which is frequently adopted in InSAR processing, has been applied for the mitigation of phase distortion by the orbital error. However, the interferogram still has low frequency of Sin pattern along the azimuth direction. In contrast, after we applied the proposed method for ionospheric correction, the low frequency pattern is mitigated and the profile results has stable phase variation values within ${\pm}1rad$. Our results show that this method provides a promising way to correct orbital and ionospheric artifact and would be important technique to improve the accuracy and the availability for L-band or P-band systems.

A Study on Extending Successive Observation Coverage of MODIS Ocean Color Product (MODIS 해색 자료의 유효관측영역 확장에 대한 연구)

  • Park, Jeong-Won;Kim, Hyun-Cheol;Park, Kyungseok;Lee, Sangwhan
    • Korean Journal of Remote Sensing
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    • v.31 no.6
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    • pp.513-521
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    • 2015
  • In the processing of ocean color remote sensing data, spatio-temporal binning is crucial for securing effective observation area. The validity determination for given source data refers to the information in Level-2 flag. For minimizing the stray light contamination, NASA OBPG's standard algorithm suggests the use of large filtering window but it results in the loss of effective observation area. This study is aimed for quality improvement of ocean color remote sensing data by recovering/extending the portion of effective observation area. We analyzed the difference between MODIS/Aqua standard and modified product in terms of chlorophyll-a concentration, spatial and temporal coverage. The recovery fractions in Level-2 swath product, Level-3 daily composite product, 8-day composite product, and monthly composite product were $13.2({\pm}5.2)%$, $30.8({\pm}16.3)%$, $15.8({\pm}9.2)%$, and $6.0({\pm}5.6)%$, respectively. The mean difference between chlorophyll-a concentrations of two products was only 0.012%, which is smaller than the nominal precision of the geophysical parameter estimation. Increase in areal coverage also results in the increase in temporal density of multi-temporal dataset, and this processing gain was most effective in 8-day composite data. The proposed method can contribute for the quality enhancement of ocean color remote sensing data by improving not only the data productivity but also statistical stability from increased number of samples.

Program Development for Automatic Extraction and Transformation of Standard Metadata of Geo-spatial Data (공간정보 표준 메타데이터 추출 및 변환 프로그램 개발)

  • Han, Sun-Mook;Lee, Ki-Won
    • Korean Journal of Remote Sensing
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    • v.26 no.5
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    • pp.549-559
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    • 2010
  • In geo-spatial information system building and operation, metadata is one of the crucial factors. Therefore, international and domestic organizations or associations for standardization have developed and distributed geo-based standard metadata to meet public demands. However, because metadata is composed of complicated elements and needs XML storage and management, individual organization which implement and operate practical application system is inclined to define and use its own metadata specifications. In this study, metadata extraction program, that metadata elements are directly extracted from geo-based file formats was developed to easily utilize standard metadata such as ISO/TC 19115, TTAS.KO-10.0139 and TTAS.IS-19115, and those elements are processed into XML. Furthermore, geo-based images sets are applied to another metadata of ISO/TC 19115-2. As well, metadata transformation is needed due to inconsistent or non-corresponding definition among standard metadata; in this program, transformation modules are also implemented to interoperable uses between standard metadata specifications. Widely used data formats are dealt with in this program, but extension for other formats and other metadata specifications is possible, and it is expected that availability of standard metadata is increased, through this kind of development.

A Study on forest fires Prediction and Detection Algorithm using Intelligent Context-awareness sensor (상황인지 센서를 활용한 지능형 산불 이동 예측 및 탐지 알고리즘에 관한 연구)

  • Kim, Hyeng-jun;Shin, Gyu-young;Woo, Byeong-hun;Koo, Nam-kyoung;Jang, Kyung-sik;Lee, Kang-whan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.6
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    • pp.1506-1514
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    • 2015
  • In this paper, we proposed a forest fires prediction and detection system. It could provide a situation of fire prediction and detection methods using context awareness sensor. A fire occurs wide range of sensing a fire in a single camera sensor, it is difficult to detect the occurrence of a fire. In this paper, we propose an algorithm for real-time by using a temperature sensor, humidity, Co2, the flame presence information acquired and comparing the data based on multiple conditions, analyze and determine the weighting according to fire in complex situations. In addition, it is possible to differential management of intensive fire detection and prediction for required dividing the state of fire zone. Therefore we propose an algorithm to determine the prediction and detection from the fire parameters as an temperature, humidity, Co2 and the flame in real-time by using a context awareness sensor and also suggest algorithm that provide the path of fire diffusion and service the secure safety zone prediction.

Development of Landslide Detection Algorithm Using Fully Polarimetric ALOS-2 SAR Data (Fully-Polarimetric ALOS-2 자료를 이용한 산사태 탐지 알고리즘 개발)

  • Kim, Minhwa;Cho, KeunHoo;Park, Sang-Eun;Cho, Jae-Hyoung;Moon, Hyoi;Han, Seung-hoon
    • Economic and Environmental Geology
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    • v.52 no.4
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    • pp.313-322
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    • 2019
  • SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.