• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.275-280
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• 2002

A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.748-750
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• 1995

An early detection of cancer is very important for the complete cure of cancer. Therefore, it is considered a diagnosis of cancer via the detection of an abrupt change from the healthy state to the cancerous state. It includes the development of algorithm for the detection of parameter change for conditionally-linear stochastic systems for the cancer diagnosis. The statistical testing is proposed to implement a parameter change algorithm. The detection algorithm studied in this research is based on sequential hypotheses testing in a so-called local asymptotic framework. Here a simple numerical example is provided to highlight some of the concepts and to provide a basis for further investigation. Despite its simplicity this research may have practical application in clinical oncology.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.222-227
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• 2002

This research will examine into the capabilities of KOMPSAI-1 EOC image application in the field of urban environment and at the same time, with that as its foundation, come to understand the urban changes of the study areas. This research is constructed in three stages: Firstly, for application of change detection techniques, which utilizes multi-temporal remotely sensed data, the data normalization process is carried out. Secondly, change detection method is applied fur the systematic monitoring of land use changes, which utilizes multi-temporal EOC images. Lastly, by using the results of the application of land use changes, the existing land use map is updated. Consequently, the land-use change patterns are monitored, which utilize multi-temporal panchromatic EOC image data; and application potentials of ancillary data fur updating existing data can be presented. In this research, with the use of the land use change, monitoring of urban growth has been carried out, and the potential for the application of KOMPSAT-1 EOC images and the scope of application was examined. Henceforth, the future expansion of the scope of application of KOMPSAT-1 EOC image is anticipated.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.157-160
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• 2002

Scene change detection is the fundamental process of automatic video indexing and retrieving. In this paper we propose a method which utilizes both compressed and uncompressed domain methods to detect scene change in a video. Candidate locations for scene change are estimated from DC images and motion vector information in compressed domain. And candidate frames are verified using edge histogram distance and color histogram distance, in uncompressed domain. The experimental results show that scene change can be detected fast and correctly by proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.259-266
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• 2015

Change detection using the heterogeneous data such as aerial images, aerial LiDAR (Light Detection And Ranging), and satellite images needs to be developed to efficiently monitor the complicating land use change. We approached this problem not relying on the intensity value of the geospatial image, but by using RECC(Relative Edge Cross Correlation) which is based on the edge information over the urban and suburban area. The experiment was carried out for the aerial LiDAR data with high-resolution Kompsat-2 and −3 images. We derived the optimal window size and threshold value for RECC-based change detection, and then we observed the overall change detection accuracy of 80% by comparing the results to the manually acquired reference data.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• The Journal of Korean Institute of Information Technology
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• v.17 no.4
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• pp.109-117
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• 2019

In modern society, due to various accidents and crime threats committed to an unspecified number of people, individual security awareness is increasing throughout society and various surveillance techniques are being actively studied. Still, there is a decline in robustness due to many problems, requiring higher reliability monitoring techniques. Thus, this paper suggests a real-time change detection technique to complement the low robustness problem in various environments and dynamic/static change detection and to solve the cost efficiency problem. We used the Self-Organizing Map (SOM) applied as a data clustering technique to implement change detection, and we were able to confirm the superiority of noise robustness and abnormal detection judgment compared to the detection technique applied to the existing image surveillance system through simulation in the indoor office environment.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.389-396
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• 2001

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has a faster searching speed and lower missing scene change detection than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect missing frame and searching precision. In this study, the whole moving pictures were primarily retrieved threshold by the temporal difference of histogram scene change detection method. We suggested a dynamic threshold algorithm using cut detection interval and derived an equation formula to determine optimal primary retrieval threshold which can cut detection interval computation. Experimental results show that the proposed dynamic threshold algorithm using cut detection interval method works up about 30 percent in precision of performance than the sequential searching method.

• Journal of the Korea Computer Industry Society
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• v.8 no.1
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• pp.9-16
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• 2007

In this study, the change of neutron detection can be use the basic data of asphalt content detector under the influence of the jurisdiction and usage of radioisotopes are limited of $100[{\mu}Ci]$ or less. To obtain neutron detector's properties using design materials in first step phase, the change of neutron detection is to be calculated how can be increase or decrease due to the change of asphalt content, also it look over the change results which is installed absorber(cadmium plate) around moderator(polyethylene) using MCNP Code.