• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.4 s.310
• /
• pp.74-86
• /
• 2006

This paper proposes a robust and computationally efficient algorithm for automatic video object segmentation. For implementing the spatio-temporal segmentation, which aims for efficient combination of the motion segmentation and the color segmentation, an SOM-based hierarchical clustering method in which the segmentation process is regarded as clustering of feature vectors is employed. As results, problems of high computational complexity which required for obtaining exact segmentation results in conventional video object segmentation methods, and the performance degradation due to noise are significantly reduced. A measure of motion vector reliability which employs MRF-based MAP estimation scheme has been introduced to minimize the influence from the motion estimation error. In addition, a noise elimination scheme based on the motion reliability histogram and a clustering validity index for automatically identifying the number of objects in the scene have been applied. A cross projection method for effective object tracking and a dynamic memory to maintain temporal coherency have been introduced as well. A set of experiments has been conducted over several video sequences to evaluate the proposed algorithm, and the efficiency in terms of computational complexity, robustness from noise, and higher segmentation accuracy of the proposed algorithm have been proved.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2002.03b
• /
• pp.127-141
• /
• 2002

GIS환경에서 도시모형(urban model)의 적용을 목적으로 사회·경제적 데이터(socio-economic data)를 활용하는 과정은 도시현상이 갖는 복잡성과 변동성으로 인해 하나의 특정시간에서의 상황을 그대로 저장한 형태인 스냅샷 모형(snapshot model)만으로는 효율적인 공간분석의 실행이 불가능하다. 또한 도시모형을 적용하는 과정에서 GIS의 대상이 되는 공간, 속성, 시간의 정의는 분석목적에 따라 다르게 정의되어질 수 있으며 이에 따라 상이한 결과가 도출될 수 있다. 본 연구는 30년 간의 부산시 인구분포의 동적 변화과정 관측을 위해 시간개념을 결합한 Temporal GIS를 구축하고 이를 활용하여 인구밀도모형 및 접근성모형을 적용하는 과정을 통해 보다 효율적이고 다양한 결과를 제시할 수 있는 GIS 활용방안을 제시하고자 하였다. 흔히 공간현상의 계량화와 통계적 기법의 적용을 위한 데이터 처리과정은 많은 오차와 오류를 유발할 수 있다. 이러한 문제의 해결을 위해서는 우선적으로 분석목적에 맞는 데이터의 정의(Data Definition), 적용하고자 하는 모형(Model)의 유용성 검증, 적절한 분석단위의 설정, 결과해석의 객관적 접근 등이 요구된다. 이와 더불어 변동성 파악을 위한 시계열 자료의 효율적 처리를 위한 방법론이 마련되어져야 한다. 즉, GIS환경에서의 도시모형의 적용에 따른 효율성과 효과성의 극대화를 위해서는 분석목적에 맞는 데이터모델의 설정과 공간DB의 구축방법이 이루어져야 하며 분석가능한 데이터의 유형에 대한 충분한 고려와 적용과정에서 분석결과에 중대한 영향을 미칠 수 있는 요소들을 미리 검증하여 결정하는 순환적 의사결정과정이 필요하다., 표준패턴을 음표와 비음표의 두개의 그룹으로 나누어 인식함으로써 DP 매칭의 처리 속도를 개선시켰고, 국소적인 변형이 있는 패턴과 특징의 수가 다른 패턴의 경우에도 좋은 인식률을 얻었다.r interferon alfa concentrated solution can be established according to the monograph of EP suggesting the revision of Minimum requirements for biological productss of e-procurement, e-placement, e-payment are also investigated.. monocytogenes, E. coli 및 S. enteritidis에 대한 키토산의 최소저해농도는 각각 0.1461 mg/mL, 0.2419 mg/mL, 0.0980 mg/mL 및 0.0490 mg/mL로 측정되었다. 또한 2%(v/v) 초산 자체의 최소저해농도를 측정한 결과, B. cereus, L. mosocytogenes, E. eoli에 대해서는 control과 비교시 유의적인 항균효과는 나타나지 않았다. 반면에 S. enteritidis의 경우는 배양시간 4시간까지는 항균활성을 나타내었지만, 8시간 이후부터는 S. enteritidis의 성장이 control 보다 높아져 배양시간 20시간에서는 control 보다 약 2배 이상 균주의 성장을 촉진시켰다.차에 따른 개별화 학습을 가능하게 할 뿐만 아니라 능동적인 참여를 유도하여 학습효율을 높일 수 있을 것으로 기대된다.향은 패션마케팅의 정의와 적용범위를 축소시킬 수 있는 위험을 내재한 것으로 보여진다. 그런가 하면, 많이 다루어진 주제라

• Korean Journal of Environmental Biology
• /
• v.27 no.1
• /
• pp.114-123
• /
• 2009

The species composition and biomass of coralline algae around three (Uljin, Wolseong and Gori) nuclear power plants on the east coast of Korea were investigated seasonally from February 1997 to October 2006. As a result, 13 species of coralline algae were found during the past ten years. Among them, Corallina pilulifera, C. officinalis, Amphiroa zonata and Pneophyllum zostericolum were common species that occurred more than 50% in frequency during the study period. Species number of coralline algae were between 10$\sim$12 species at the breakwaters near the outfalls of power plants and 8$\sim$12 species at the control area, and differences in species composition were not observed among study sites. Seasonal fluctuations of mean biomass were 0$\sim$2,530 g dry wt m$^{-2}$ and dominant species in biomass was Corallina pilulifera at all the study sites. The breakwaters of power plants generally had a greater coralline algal biomass than the control area. Biomass proportions of coralline algae at the breakwaters of power plants were also higher than those at the control area. At the Uljin sites, particularly, biomass of coralline algae showed greatest in summer and biomass proportion showed highest during the summer season. Differences in the spatio-temporal patterns of coralline algae around three nuclear power plants on the east coast of Korea were presumably due to the regional temperature variations.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.3
• /
• pp.1-18
• /
• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.

• The KIPS Transactions:PartD
• /
• v.16D no.4
• /
• pp.487-496
• /
• 2009

This paper addresses an indexing scheme capable of efficiently processing range queries in a large-scale trajectory database. After discussing the drawbacks of previous indexing schemes, we propose a new scheme that divides the temporal dimension into multiple time intervals and then, by this interval, builds an index for the line segments. Additionally, a supplementary index is built for the line segments within each time interval. This scheme can make a dramatic improvement in the performance of insert and search operations using a main memory index, particularly for the time interval consisting of the segments taken by those objects which are currently moving or have just completed their movements, as contrast to the previous schemes that store the index totally on the disk. Each time interval index is built as follows: First, the extent of the spatial dimension is divided onto multiple spatial cells to which the line segments are assigned evenly. We use a 2D-tree to maintain information on those cells. Then, for each cell, an additional 3D $R^*$-tree is created on the spatio-temporal space (x, y, t). Such a multi-level indexing strategy can cure the shortcomings of the legacy schemes. Performance results obtained from intensive experiments show that our scheme enhances the performance of retrieve operations by 3$\sim$10 times, with much less storage space.

• Korean Journal of Remote Sensing
• /
• v.20 no.4
• /
• pp.245-252
• /
• 2004

The mechanism of cold core eddy formation was investigated using boundary conditions between the East China coastal cold water and the Kuroshio Warm Current, wind data related to the monsoon which was measured by QuikSCAT, and the bottom topography of the East China Sea. When winds blow from the southeast at an intensity comparable to that in the winter period in 1999 and 2003, the warm Kuroshio and Tsushima Current became stronger, and temperatures were considerably higher than those of the extended cold water of the coast of the East China. At that time, the cold water was captured by warm water from the Kuroshio and the Tsushima Current. This facilitated the formation of mesoscale cold core eddies with diameter of 150km in the East China Sea in May, 1999 and February, 2003. The cold core eddy which was detected by NOAA, SeaWiFS and QuikSCAT satellites. The East China Sea is considered to be important not only as a good fishing ground but also nursery and spawning area for many kinds of fishes. Therefore, it would be worth studying spatio-temporal variations of the cold core eddy in the environmental conditions of the northwestern East China Sea using systematic remote sensing techniques.

• Korean Journal of Remote Sensing
• /
• v.25 no.6
• /
• pp.475-486
• /
• 2009

Harmful algal blooms (HAB) caused by the dominant species Cochlodinium polykrikoides (C. polykrikoides) appear in the South Sea of Korea and are particularly present in summer and fall seasons. Environmental factors such as water temperature, weather conditions (air temperature, cloud cover, sunshine, precipitation and wind) influence on the initiation and subsequent development of HAB. The purpose of this research was to study spatial and temporal variations of HAB in the Yeosu area using environmental (oceanic and meteorological) and satellite data. Chlorophyll-a concentrations were calculated using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images by an Ocean Chlorophyll 4 (OC4) algorithm, and HAB were estimated using the Red tide index Chlorophyll Algorithm (RCA). We also used the surface velocity of sequential satellite images applying the Maximum Cross Correlation method to detect chlorophyll-a movement. The results showed that the water temperature during HAB occurrences in August 2002-2008 was $19.4-30.2^{\circ}C$. In terms of the frequency of the mean of cell density of C. polykrikoides, the cell density of the HAB found at low (<300 cells/ml), medium (300-1000 cells/ml), and high (>1000 cells/ml) levels were 27.01%, 37.44%, and 35.55%, respectively. Meteorological data for 2002-2008 showed that the mean air temperature, precipitation, wind speed and direction, and sunshine duration were $22.39^{\circ}C$, 6.54 mm/day, 3.98 m/s (southwesterly), and 1-11.7 h, respectively. Our results suggest that HAB events in the Yeosu area can be triggered and extended by heavy precipitation and massive movement of HAB from the East China Sea. Satellite images data from July to October 2002-2006 showed that the OC4 algorithm generally estimated high chlorophyll-a concentration ($2-20\;mg/m^3$) throughout the coastal area, whereas the RCA estimated concentrations at $2-10\;mg/m^3$. The surface velocity of chlorophyll-a movement from sequential satellite images revealed the same patterns in the direction of the Tsushima Warm Current.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1507-1515
• /
• 2021

If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.

• Journal of Environmental Science International
• /
• v.27 no.11
• /
• pp.1129-1140
• /
• 2018

Temporal and spatial characteristics of the frequency of several weather types and the change in air pollutant concentrations according to these weather types were analyzed over a decade (2007-2016) in seven major cities and a remote area in Korea. This analysis was performed using hourly (or daily) observed data of weather types (e.g., mist, haze, fog, precipitation, dust, and thunder and lighting) and air pollutant criteria ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, CO, and $SO_2$). Overall, the most frequent weather type across all areas during the study period was found to be mist (39%), followed by precipitation (35%), haze (17%), and the other types (${\leq}4%$). In terms of regional frequency distributions, the highest frequency of haze (26%) was in Seoul (especially during winter and May-June), possibly due to the high population and air pollutant emission sources, while that of precipitation (47%) was in Jeju (summer and winter), due to its geographic location with the sea on four sides and a very high mountain. $PM_{10}$ concentrations for dust and haze were significantly higher in three cities (up to $250{\mu}g/m^3$ for dust in Incheon), whereas those for the other four types were relatively lower. The concentrations of $PM_{2.5}$ and its major precursor gases ($NO_2$ and $SO_2$) were higher (up to $69{\mu}g/m^3$, 48 ppb, and 16 ppb, respectively, for haze in Incheon) for haze and/or dust than for the other weather types. On the other hand, there were no distinct differences in the concentrations of $O_3$ and CO for the weather types. The overall results of this study confirm that the frequency of weather types and the related air quality depend on the geographic and environmental characteristics of the target areas.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.11-19
• /
• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.