• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.4
• /
• pp.90-103
• /
• 2012

Mallipo is a representative beach contaminated by Hebei Spirit oil spill accident in December 2007. This study aims to compare the differences of two seasons (winter and summer) for the spatio-temporal variation patterns of oiling status in the whole area and divided five regions of Mallipo beach. In the whole area, the decreasing rate of average TPH (total petroleum hydrocarbon) in winter was twice greater than summer during four years. According to the spatial variation pattern analysis of oiling status using weighted mean center and weighted standard distance, the oil concentration was clustered on southwestern region in winter, however, the TPH was dispersed in the whole area in summer. Temporal variation pattern of TPH in each of Mallipo's five regions showed that TPH had been consistently decreased in winter, but oil concentration had not been changed in summer since 2009 except the southwestern region. Therefore, in order to evaluate and predict the progress of oiling status, it is needed to analyze the spatio-temporal variation pattern of TPH using spatial analysis after separating data into seasons (e.g., winter and summer). In addition, time series analysis is useful in the regional scales through spatial partitioning rather than the whole beach area for the understanding of temporal variation pattern.

• International Journal of Contents
• /
• v.7 no.4
• /
• pp.19-23
• /
• 2011

This paper proposes a segmentation method for overlapped vehicles based on analysis of the vehicle location and the spatiotemporal association information. This method can be used in an intelligent transport system. In the proposed method, occlusion is detected by analyzing the association information based on a vehicle's location in continuous images, and occlusion segmentation is carried out by using the vehicle information prior to occlusion. In addition, the size variations of the vehicle to which association tracking is applied can be anticipated by learning the variations according to the overlapped vehicles' movements. To assess the performance of the suggested method, image data collected from CCTVs recording traffic information is used, and average success rate of occlusion segmentation is 96.9%.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10a
• /
• pp.57-59
• /
• 2000

실세계의 객체들은 공간 정보뿐만 아니라 시간적 정보와도 연관을 갖는데 기존의 공간데이터베이스만으로는 시간 흐름에 따른 공간 객체의 정보를 효율적으로 관리해 주지 못하는 단점이 있다. 이러한 문제점을 해결하기 위하여 시공간 데이터베이스에 대한 데이터 모델과 시공간 연산자가 제시되었다. 그러나, 시공간 연산자에 대한 정의와 시그너처정도 만이 기술되었고 시공간 연산자에 대한 설계와 구현에 대한 사항은 제시되지 않았다. 이 논문에서는 시공간 데이터모델과 시공간 연산자 그리고 공간 연산자 구현 기법인 Planc-Sweep 기법을 이용한 시공간 연산자인 Trajectory와 MPIntersection에 대한 설계와 구현 알고리즘을 제시하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.2
• /
• pp.24-31
• /
• 2011

This paper proposes spatio-temporal denoising algorithm based on nonlocal means. Though the conventional denoising algorithms based on nonlocal means have good performance in noise removal, it is difficult to implement them into the hardware system due to much computational load and the need for several frame buffers. Therefore we adopted infinite impulse response temporal noise reduction algorithm in the proposed algorithm. Proposed algorithm shows less artificial denoised result in the motionless region. In the motion region, spatial filter based on efficiently improved nonlocal means algorithm conduct noise removal with less motion blur. Experimental results including comparisons with conventional algorithms for various noise levels and test images show the proposed algorithm has a good performance in both visual and quantitative criteria.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.4
• /
• pp.3-12
• /
• 2011

Understanding of water sediment trends is an important part of water quality monitoring. Water quality variables change over time and space, and cannot be modeled or explained clearly by either temporal or spatial analysis alone. This research analysed the trends of temperature, pH levels and dissolved oxygen levels based on the sediment records and spatial data obtained in Lower Mekong Basin (LMB) during 1985-2005. Our aim is to evaluate spatio-temporal trends and graphical analyses using an Inverse Distance Weighting (IDW) interpolation method. The main results from this research can be summarized as follows. The maximum temperature and pH have been stable during the study period and the maximum dissolved oxygen has been increasing gradually until 2002. The minimum pH and dissolved oxygen have been changing in an unsteady trend during the period. A spatial analysis shows that the water temperature in this region has been increasing over time. The pH trend shows that it is decreasing during 1993-2005. Dissolved oxygen concentration has been increasing from 1989 onwards and stays in that track.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.8
• /
• pp.67-80
• /
• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.69-82
• /
• 2004

A moving object has a special feature that it's spatial location, shape and size are changed as time goes. These changes of the object accompany the continuous movement that is called the trajectory. In this paper, we propose an index structure that users can retrieve the trajectory of a moving object with the access of a page. We also propose the multi-complex query that is a new query type for trajectory retrieval. In order to prove the excellence of our method, we compare and analyze the performance for query time and storage space through experiments in various environments. It is shown that our method outperforms the existing index structures when processing spatio-temporal trajectory queries on moving objects.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.1
• /
• pp.57-61
• /
• 2009

To increase the efficiency of mining process and derive accurate spatio-temporal patterns, continuous values of attributes should be discretized prior to mining process. In this paper, we propose a discretization method which improves the mining efficiency by reducing the data size without losing the correlations in the data. The proposed method first s original trajectories into approximations using line simplification and then groups them into similar clusters. Our experiments show that the proposed approach improves the mining efficiency as well as extracts more intuitive patterns compared to existing discretization methods.

• Journal of Digital Contents Society
• /
• v.15 no.4
• /
• pp.527-532
• /
• 2014

Recently, due to the proliferation of mobile smart devices, the inovation of bigdata, which analyzes and processes massive data collected from various sensors implaned in smart devices, expands to LBSs. Many location prediction techniques for moving objects have been studied in literature. However, as the majority of studies perform location prediction which depends on specific applications, they hardly reflect the technical requirements of next-generation spatio-temporal information services. Therefore, this paper proposes the design of general-purpose distributed moving object prediction query processing framework that is capable of performing primitive and various types of queries effectively based on massive spatio-temporal data of moving objects in real-world space networks.

• Smart Structures and Systems
• /
• v.14 no.1
• /
• pp.1-16
• /
• 2014

In this work, we address the so-called sensor reachback problem for Wireless Sensor Networks, which consists in collecting the measurements acquired by a large number of sensor nodes into a sink node which has major computational and power capabilities. Focused on applications such as Structural Health Monitoring, we propose a cooperative communication protocol that exploits the spatio-temporal correlations of the sensor measurements in order to save energy when transmitting the information to the sink node in a non-stationary environment. In addition to cooperative communications, the protocol is based on two well-studied adaptive filtering techniques, Least Mean Squares and Recursive Least Squares, which trade off computational complexity and reduction in the number of transmissions to the sink node. Finally, experiments with real acceleration measurements, obtained from the Canton Tower in China, are included to show the effectiveness of the proposed method.