• IEIE Transactions on Smart Processing and Computing
• /
• v.2 no.3
• /
• pp.117-129
• /
• 2013

The increasing demand for monitoring wildfires and their impact on the land surface have prompted studies of burned area extraction and analysis. To differentiate burned and unburned area, the earlier method of the Moderate Resolution Imaging Spectro-radiometer (MODIS) Burned Area Detection Algorithm was proposed to estimate the change in land surface based on the reflectance energy. The energy, whose wavelengths are sensitive to burning, was selected to calculate the change parameter $Z_{score}$. This method was applied using the MODIS images to produce a MODIS Burned Area product. The approach was to simplify this algorithm to make it compatible with the Landsat 7 ETM+ SLC-off images. To extract the refined version of burned regions, post-processing was carried out by applying a median filter, dilation morphology algorithm, and finally a gap filling method. The experimental results showed that the detailed burned areas extracted from the proposed method exhibited more spatial details than those of the MODIS Burned products in the large U.S areas. The results also revealed the discontinuous distribution of burned regions in Vietnam forests.

• The KIPS Transactions:PartB
• /
• v.12B no.7 s.103
• /
• pp.761-766
• /
• 2005

This paper proposes a video model for the implementation of the cultural video database system. We have utilized an efficient scene change detection method that segments cultural video into semantic units for efficient indexing and retrieval of video. Since video has a large volume and needs to be played for a longer time, it implies difficulty of viewing the entire video. To solve this Problem. the cultural video abstraction was made to save the time and widen the choices of video the video abstract is the summarization of scenes, which includes important events produced by setting up the abstraction rule.

• KSCI Review
• /
• v.14 no.1
• /
• pp.255-264
• /
• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with ${\chi}^2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.7-10
• /
• 2000

There are many Problems such as low detection ratio, velocity and increase of false hit ratio on the detection of gradual scene changes with the previous shot transition detection algorithms. In this paper, we Propose an improved dissolve detection method using color information on low-frequency subband and edge elements on high-frequency subband. The Possible dissolve transition are found by analyzing the edge change ratio in the high-frequency subband with edge elements of each direction. Using the double chromatic difference on the lowest frequency subband, we have the improvement of the dissolve detection ratio. The simulation results show that the performance of the proposed algorithm is better than the conventional one for dissolve detection on a diverse set of uncompressed video sequences.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.spc8
• /
• pp.3171-3174
• /
• 2011

• Smart Structures and Systems
• /
• v.21 no.5
• /
• pp.705-713
• /
• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• Journal of Korean Society for Geospatial Information Science
• /
• v.13 no.2 s.32
• /
• pp.3-12
• /
• 2005

Change detections are widely used for its usefulness. During the process two factors are important; one is which method is to be adopted and the other is what should be the appropriate critical value. Until far, these factors are mostly decided by users based upon their knowledge from past experiences. In this paper we propose a set of methodologies that allow users maintaining optimal decisions on which change detection method is the desirable one, what might be the suitable critical value, and what does the introducing SDSSs(Spatial Decision Support Systems) to change detections means.

• Journal of Korean Society for Geospatial Information Science
• /
• v.12 no.2 s.29
• /
• pp.43-52
• /
• 2004

This study presents the application of aerial photographs and the Korea Multi-Purpose Satellite, KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting change in an urban area that has been rapidly growing. For the study, we used multi-temporal images which were acquired by two different sensors. Image registration and resampling were rallied out before performing change detection in a common reference system with the same spatial resolution. for all of the images. Results from image differencing and image ratioing techniques show that panchromatic aerial photographs and KOMPSAT-1 EOC images collected by different sensors have potential to detect changes of urban features such as building, road and other man-made structure. And the optimal threshold values were suggested in applying image differencing and image ratioing techniques for change detection.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.1
• /
• pp.717-724
• /
• 2016

This paper proposes a change detection method based on the covariance matrices of color and edge gradient in a color video. The YCbCr color format was used instead of RGB. The color covariance matrix was calculated from the CbCr-channels and the edge gradient covariance matrix was calculated from the Y-channels. The covariance matrices were effectively calculated at each pixel by calculating the sum, squared sum, and sum of two values' multiplication of a rectangle area using the integral images from a background image. The background image was updated by a running the average between the background image and a current frame. The change areas in a current frame image against the background were detected using the Mahalanobis distance, which is a measure of the statistical distance using covariance matrices. The experimental results of an expressway color video showed that the proposed approach can effectively detect change regions for color and edge gradients against the background.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.371-384
• /
• 2022

The fused LASSO signal approximator (FLSA) can be applied to find change points from the data having piecewise constant mean structure. It is well-known that the FLSA is inconsistent in change points detection. This inconsistency is due to a total-variation denoising penalty of the FLSA. ℓ₁ trend filter, one of the popular tools for finding an underlying trend from data, can be used to identify change points of piecewise linear trends. Since the ℓ₁ trend filter applies the sum of absolute values of slope differences, it can be inconsistent for change points recovery as the FLSA. However, there are few studies on the inconsistency of the ℓ₁ trend filtering. In this paper, we demonstrate the inconsistency of the ℓ₁ trend filtering with a numerical study.