• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.131-137
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• 2004

This study presents the application of multi-temporal aerial photographs in detecting change in a small urban area. For the panchromatic aerial images of the scale of 1/20000 and 1/37500 photographed in 1987, 1996 and 2000, image geometric correction and registration were carried out before performing change detection in a common reference system and then image mosaicking. The image differencing technigue was employed to detect urban features and landcover change and then the results were compared to those of image ratioing techniques. Also threshold values were suggested in applying image differencing for change detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.119-124
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• 2003

In this paper, we modeled positive selection and negative selection that is developing process of cytotoxic T-cell that plays important role in biological immune system. Also, we developed change detection algorithm, which is very Important part in detecting data change by intrusion and data infection by computer virus. Proposed method is the algorithm that produces MHC receptor lot recognizing self and antigen detector for recognizing non-self. Therefore, proposed method detects self and intruder by two type of detectors like real immune system. We show the effectiveness and characteristics of proposed change detection algorithm by simulation about point and block change of self file.

• Korean Journal of Materials Research
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• v.28 no.1
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• pp.1-5
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• 2018

Gas detection is necessary for various reasons, including the prevention of gas leakages and the creation of necessary environmental conditions. Among the gas detection methods, leakage of gas can be confirmed using materials that undergo color changes that are easily distinguished by the naked eye. Metal nanoparticles (NPs) experience variations in their absorption wavelengths under the localized surface plasmon effect (LSPR) with mechanical stresses, which change the distance between NPs. In this study, we attempted to detect the presence of gas utilizing the LSPR-related color change of a chain of Au NPs. The assembly of Au NPs, arranged in a chain shape, experienced a color change from dark blue to purple with a change in the distance between the NPs by applying a physical force, i.e., compression, stretching, and gas pressure. As the force of compression and the degree of stretching increased, the absorption wavelength shifted from doublet peaks at 650 and 550 nm to a singlet peak at 550 nm. Further, applying gas pressure caused an identical color change. With this result, we propose a method that could be applied to all gases that require detection based on gas pressure.

• Journal of the Korea Society of Computer and Information
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• v.4 no.3
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• pp.28-34
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• 1999

In this paper. we propose the method of Scene Change Detection for video sequence using the DC components and the moving vectors in the Macro Blocks in the DCT blocks. The proposed method detects the Scene Change which would not be related with the specific sequences in the compressed MPEG domain. To do this. we define new metrics for Scene Change Detection using the features of picture component and detect the exact Scene Change point of B-pictures using the characteristics of B-picture's sharp response for the moving vectors. In brief, we will detect the cut point using I-picture and the gradual scene changes such as dissolve, fade, wipe, etc. As a results, our proposed method shows good test results for the various MPEG sequences.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.116-124
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• 2008

This study presents the application of multi-temporal and multi-scale panchromatic aerial photos for change detection in a small urban area. For aerial photos of the scale of 1:20,000 taken in 1987 and 1996 and the scale of 1:37,500 taken in 2000. Pre-processing that make the same conditions to all of the aerial photos was carried out through geometric correction, registration, contrasting, resamplimg, and mosaicking and then change detection were carried out respectively by image differencing and image ratio techniques. As a result, the change of urban features and landcover were able to be detected from panchromatic aerial photos that is single-band images and then the detected change results were compared between both techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.71-80
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• 2011

Thresholding is important step for detecting binary change/non-change information in the unsupervised change detection. This study proposes new unsupervised change detection method using Hyperion hyperspectral images, which are expected with data increased demand. A graph is drawn with applying the range average method for the result value through pixel-based similarity measurement, and thresholding value is decided at the maximum distance point from a straight line. The proposed method is assessed in comparison with expectation-maximization algorithm, coner method, Otsu's method using synthetic images and Hyperion hyperspectral images. Throughout the results, we validated that the proposed method can be applied simply and had similar or better performance than the other methods.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.777-786
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• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Journal of Korea Multimedia Society
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• v.7 no.7
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• pp.877-885
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• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.47-52
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• 2003

Change detection and analysis is a powerful application of remote sensing, in that the spectral resolution of multi-band sensors can be used to advantage in monitoring both significant and subtle land cover changes over time. In this study, the LANDSAT TM data was used to detect the change areas affected by flood from a heavy rainfall. The study area is the Nakdong River located in the Korea peninsular. Among the several change detection techniques, change vector analysis(CVA), principle component analysis(PCA) and image difference approach are utilized in this paper. CVA uses any number of spectral bands from multi-date satellite data to produce change image that yield information of the magnitude and direction of differences pixel values. And accuracy assessment was carried out with a change image produced from three techniques. In result, CVA was found to be the most accurate for detecting areas affected by flood. CVA with the overall accuracy and Kappa coefficient of 97.27 percent and 94.45 percent, respectively.