• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.25 no.2
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• pp.521-533
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• 1995

This study was performed to develop and evaluate the method to detect Quantitatively the serial changes in the size of artificial lesion in the spongious bone by automatic color image analyzer. 15 intraoral radiograms taken before and after endodontic treatment of 5 cases were used for contour line analysis. 30 intraoral radiograms taken by geometrically standardized apparatus before and after serially the formation of artificial lesions of 0.80, 1.20, 1.75, 2.00mm in diameter at the periapical area and interdental area of spongious bone were used. The analysis of image according to the variance of lesion size by 0.25, 0.35, 0.55, and 0.85mm serially was performed by the histogram and the color enhancement with subtraction. The images inputted by CCDcamera were digitized and analyzed by NEXUS QUBE program with NEC PC-9801 computer. The obtained results were as follows: 1. There was no reliability in the analysis of lesions by contour line 2 .. The mean difference of the grey scale at each pixel was 1 step between reference image and the corrected images. 3. In the analysis by histogram of the artificial lesion in spongeous bone, the change over 0.55mm in the mesiodistal size was detectable by the change of the numbers of pixel showing the change in grey scale. 4. In the analysis by histogram of the artificial lesion in spongeous bone, the change over 0.25mm in the buccolingual size was detectable by the change in grey scale. 5. By color enbancement with- subtraction, each lesion was able to be isolated and the change in it's mesiodistal size was detectable visually , but not in it's buccolingual size.

• Journal of radiological science and technology
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• v.41 no.2
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• pp.115-122
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• 2018

We aims to perform comparative analysis on the dose area and image qualities varying on the slice thickness when using Automatic Exposure Controller (AEC) and manual exposure; thus, it wants to suggest a measure to reduce exposure dose by setting the optimal examination condition for each slice thickness. The method was to set the thickness as Thin, Normal, and Heavy adult and evaluate the dose area, spatial resolution, low contrast resolution, Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) according to each slice thickness by using the AEC and the manual exposure controller. The dose area according to each slice thickness all increased both when using the AEC and the manual exposure. However, the manual exposure showed lower dose area product than the AEC. Spatial resolutions and low contrast resolutions were all observed to be higher than the evaluation standard. Also, the SNR and CNR of each thickness all increased when using the AEC. When using the manual exposure, SNR and CNR increased in all cases other than the Heavy Adult. Consequently, the Thin and Normal Adult showed dose reduction about 2 times when using the manual exposure controller, while ensuring the image quality. Heavy adult was able to maintain good image quality by using AEC.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.45-53
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• 2013

Global warming causes sea levels to rise and global changes apparently taking place including coastline changes. Coastline change due to sea level rise is also one of the most significant phenomena affected by global climate change. Accordingly, Coastline change detection can be utilized as an indicator of representing global climate change. Generally, Coastline change has happened mainly because of not only sea level rise but also artificial factor that is reclaimed land development by mud flat reclamation. However, Arctic coastal areas have been experienced serious change mostly due to sea level rise rather than other factors. The purposes of this study are automatic extraction of coastline and identifying change. In this study, in order to extract coastline automatically, contrast of the water and the land was maximized utilizing modified NDWI(Normalized Difference Water Index) and it made automatic extraction of coastline possibile. The imagery converted into modified NDWI were applied image processing techniques in order that appropriate threshold value can be found automatically to separate the water and land. Then the coastline was extracted through edge detection algorithm and changes were detected using extracted coastlines. Without the help of other data, automatic extraction of coastlines using LANDSAT was possible and similarity was found by comparing NLCD data as a reference data. Also, the results of the study area that is permafrost always frozen below $0^{\circ}C$ showed quantitative changes of the coastline and verified that the change was accelerated.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.166-169
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• 2004

Change detection is widely used taxation, military fields, etc. In general, global change detection methods using image difference method, etc, are used in low resolution images and local change detection methods using floating windows, etc, are used in high resolution images. But, these methods have disadvantages in practical use and automatic method for changed area detection should be developed. In this research, characteristics of Kompsat images are reviewed in perspective of change detection and various change detection method applicable to are tested.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.15-17
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• 2006

This paper presents a new global intensity compensation method for extracting moving object in a visual surveillance system by compensating time variant intensity changes of background region. The method that compensates a little changes of intensity due to time variant illumination change and automatic gain control of camera is called global intensity compensation. The proposed method expresses global intensity change with a mapping table to describe complex form of intensity change while the previous method models this global intensity change with a simple function as a straight line. The proposed method builds the mapping table by calculating the cross histogram between two images and then by selecting an initial point for generating the mapping table by using Hough transform applied to the cross histogram image. Then starting from the initial point, the mapping table is generated according to the proposed algorithm based on the assumption that reflects the characteristic of global intensity change. Experimental results show that the proposed method makes the compensation error much smaller than the previous GIC method

• Journal of Environmental Impact Assessment
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• v.22 no.1
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• pp.19-26
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• 2013

The propose of this study is to analyze the optimum spatial resolution of the urban spatial thermal environment structure and to evaluate of the possibility detection using aerial photographs and thermal satellite images. The proper techniques of the optimum spatial resolution for the urban spatial thermal environment structure were analyzed. Thermal infrared satellite image of Seoul city were used for the change rate of surface temperature variation and suggested to the spatial extent and effects of urban surface characteristics and spatial data was interpreted as regions. To extract the surface temperature, Landsat thermal infrared satellite image compared with an automatic weather station data and in the field of the measured temperature and surface temperature by thermal environment affects, the spatial domain has been verified. The surface temperature of the satellite images to extract after adjusting surface temperature isotherms were constructed. The changes in surface temperature from 2008 to 2012 the average surface temperature observation images of changing areas were divided into space. The results of this study are as follows: Through analysis of satellite imagery, Seoul city surface temperature change due to extraction comfort indices were classified into four grades. The comfort index indicative of the temperature of Gangnam-gu, $23.7{\sim}27.2(^{\circ}C)$ range and Songpagu, a $22.7{\sim}30.6(^{\circ}C)$ respectively, the surface temperature of Yeouido $25.8{\sim}32.6(^{\circ}C)$ were in the range.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.164-169
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• 2003

The purpose of this study is to develop a practical image inspect ion system that could recognize it correctly, endowing flexibility to the productive field, although the same object for work will be changed in the size and rotated. In this experiment, it selected a fighter, rotating the direction from 30$^{\circ}$ to 45 simultaneously while changing the size from 1/4 to 1/16, as an object inspection without using another hardware for exclusive image processing. The invariant moments, Hu has suggested, was used as feature vector moment descriptor. As a result of the experiment, the image inspect ion system developed from this research was operated in real-time regardless of the chance of size and rotation for the object inspection, and it maintained the correspondent rates steadily above from 94％ to 96％. Accordingly, it is considered as the flexibility can be considerably endowed to the factory automat ion when the image inspect ion system developed from this research is applied to the product ive field.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1067-1076
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• 2020

In this paper, an unsupervised saliency guided change detection method using UAV and aerial imagery is proposed. Regions that are more different from other areas are salient, which make them more distinct. The existence of the substantial difference between two images makes saliency proper for guiding the change detection process. Change Vector Analysis (CVA), which has the capability of extracting of overall magnitude and direction of change from multi-spectral and temporal remote sensing data, is used for generating an initial difference image. Combined with an unsupervised CVA and the saliency, Principal Component Analysis(PCA), which is possible to implemented as the guide for change detection method, is proposed for UAV and aerial images. By implementing the saliency generation on the difference map extracted via the CVA, potentially changed areas obtained, and by thresholding the saliency map, most of the interest areas correctly extracted. Finally, the PCA method is implemented to extract features, and K-means clustering is applied to detect changed and unchanged map on the extracted areas. This proposed method is applied to the image sets over the flooded and typhoon-damaged area and is resulted in 95 percent better than the PCA approach compared with manually extracted ground truth for all the data sets. Finally, we compared our approach with the PCA K-means method to show the effectiveness of the method.