• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.509-516
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• 2004

Semantic interpretation of image is incomplete without some mechanism for understanding semantic content that is not directly visible. For this reason, human assisted content-annotation through natural language is an attachment of textual description to image. However, keyword-based retrieval is in the level of syntactic pattern matching. In other words, dissimilarity computation among terms is usually done by using string matching not concept matching. In this paper, we propose a method for computerized semantic similarity calculation In WordNet space. We consider the edge, depth, link type and density as well as existence of common ancestors. Also, we have introduced method that applied similarity measurement on semantic image retrieval. To combine wi#h the low level features, we use the spatial color distribution model. When tested on a image set of Microsoft's 'Design Gallery Line', proposed method outperforms other approach.

• Journal of Platform Technology
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• v.9 no.4
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• pp.60-70
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• 2021

This paper proposes a method capable of stably tracking the coordinates of a patterned laser image in a curved screen shooting system using two or more channels of multiple cameras. This method can track and acquire target points very effectively when applied to a multi-screen shooting method that can replace the HMD shooting method. Images of curved screens with severe deformation obtained from individual cameras are corrected through image normalization, image binarization, and noise removal. This corrected image is created and applied as an Euclidean space map that is easy to track the firing point based on the matching point. As a result of the experiment, the image coordinates of the pattern laser were stably extracted in the curved screen shooting system, and the error of the target point position of the real-world coordinate position and the broadband Euclidean map was minimized. The reliability of the proposed method was confirmed through the experiment.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.103-111
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• 2007

The spectral information based image analysis, visual interpretation and automatic classification have been widely carried out so far for remote sensing data processing. Yet recently, many researchers have tried to extract the spatial information which cannot be expressed directly in the image itself. Using the texture and wavelet scheme, we made a wavelet-based texture fusion image which includes the advantages of each scheme. Moreover, using these schemes, we carried out image classification for the urban spatial analysis and the geological structure analysis around the caldera area. These two case studies showed that image classification accuracy of texture image and wavelet-based texture fusion image is better than that of using only raw image. In case of the urban area using high resolution image, as both texture and wavelet based texture fusion image are added to the original image, the classification accuracy is the highest. Because detailed spatial information is applied to the urban area where detail pixel variation is very significant. In case of the geological structure analysis using middle and low resolution image, the images added by only texture image showed the highest classification accuracy. It is interpreted to be necessary to simplify the information such as elevation variation, thermal distribution, on the occasion of analyzing the relatively larger geological structure like a caldera. Therefore, in the image analysis using spatial information, each spatial information analysis method should be carefully selected by considering the characteristics of the satellite images and the purpose of study.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.11-20
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• 2006

This paper presents a method for detecting tumors on chicken carcasses by fusion of hyperspectral fluorescence and reflectance images. Classification of normal skin and tumor is performed by the image obtain 어 from optimal band ratio which minimizes the overlapping area of PDFs for normal skin and tumor. This method yields four feature images, each of them represents the ratio of two intensity values from a pixel. Classification is achieved by applying ISODATA to each pixel from the feature images. For the analysis of reflectance image, band selection method is proposed based on the information quantity, many effective features are acquired for the classification by defining the linear transformation selecting the projection axis, accordingly, accurate interpretation of images is possible in the reflectance image and automatic feature selection method is realized. Feature images from reflectance images are also classified by ISODATA and combined with the result from fluorescence images. Experimental result indicates that improved performance in term of reducing false detection rate is observed.

• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.335-340
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• 2013

Purpose: This study was performed to develop a lateral flow strip sensor for the detection of pathogenic Escherichia coli O157:H7 in various samples. Also, feasibility of using an image analysis method to improve the interpretation of the strip sensor was evaluated. Methods: The lateral flow strip sensor has been fabricated based on nitrocellulose lateral-flow membrane. Colloidal gold and E. coli O157:H7 antibodies were used as a tag and a receptor, respectively. Manually spotted E. coli O157:H7 antibody and anti-mouse antibody on nitrocellulose membrane were used as test and control dots, respectively. Feasibility of the lateral flow strip sensor to detect E. coli O157:H7 were evaluated with serially diluted E. coli O157:H7 cells in PBS or food samples. Test results of the lateral flow strip sensor were measured with an image analysis method. Results: The intensity of the test dot started to increase with higher concentration of the cells were introduced. The sensitivities of the sensor were both $10^4$ CFU/mL Escherichia coli O157:H7 spiked in PBS and in chicken meat extract, respectively. Conclusions: The lateral flow strip sensor and image analysis method could detect E. coli O157:H7 in 20 min, which is significantly quicker than conventional plate counting method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.47-53
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• 2007

Satellite image mosaicking is essential for image interpretation and analysis especially for a large area such as the Korean Peninsula. This paper proposed the technique of automatic seam-line extraction and the method of creating image mosaic in automated fashion. The seam-line to minimize artificial discontinuity was extracted using Minimum Absolute Gray Difference Sum algorithm with constraint condition on search-area width and Canny Edge Detection algorithm. To maintain the radiometric balance among images acquired at different time epochs, we utilized Match Cumulative Frequency method. Experimental results showed that edge detection algorithm extracted the seam-lines significantly well along linear features such as roads and rivers.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.133-150
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• 1997

The purpose of this paper is to develop an algorithm of classification and interpretation of seafloor based on side scan sonar data. The algorithm consists of mosaicking of sonar data using navigation data, correction and compensation of the acouctic amplitude data considering the charateristics of the side scan sonar system, and segmentation of the seafloor using digital image processing techniques. The correction and compensation process is essential because there is usually difference in acoustic amplitudes from the same distance of the port-side and the starboard-side and the amplitudes become attenuated as the distance is increasing. In this paper, proposed is an algorithm of compensating the side scan sonar data, and its result is compared with the mosaicking result without any compensation. The algorithm considers the amplitude characteristics according to the tow-fish's depth as well as the attenuation trend of the side scan sonar along the beam positions. This paper also proposes an image segmentation algorithm based on the texture, where the criterion is the maximum occurence related with gray level. The preliminary experiment has been carried out with the side scan sonar data and its result is demonstrated.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.41-45
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• 2010

Image processing techniques are fundamental in human vision-based image information processing. There have been widely studied areas such as image transformation, image enhancement, image restoration, and image compression. One of research subgoals in those areas is enhancing image information for the correct information retrieval. As a fundamental task for the image recognition and interpretation, image enhancement includes noise filtering techniques. Conventional filtering algorithms may have high noise removal rate but usually have difficulty in conserving boundary information. As a result, they often use additional image processing algorithms in compensation for the tradeoff of more CPU time and higher possibility of information loss. In this paper, we propose a Fuzzy Mask Filtering algorithm that has high noise removal rate but lesser problems in above-mentioned side-effects. Our algorithm firstly decides a threshold based on fuzzy logic with information from masks. Then it decides the output pixel value by that threshold. In a designed experiment that has random impulse noise and salt pepper noise, the proposed algorithm was more effective in noise removal without information loss.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.97-114
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• 2020

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

• Economic and Environmental Geology
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• v.54 no.1
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• pp.91-103
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• 2021

In order to estimate the vertical and horizontal structural in the Yangsan fault core line (Naengsuri area, Pohang), we carried out gravity field measurements and interpretation procedures such as Euler deconvolution method and curvature analysis in addition to the forward modelling technique (i.e. IGMAS+). We found a prominent gravity difference of more than 1.5 mGal across the fault core. This indicates a distinct density difference between the western and eastern crustal area across the Yangsan fault line. Comparing this gravity field interpretation with other existent geologic and geophysical survey data (e.g. LiDAR, trenching, electric resistivity measurements), It is concluded that (1) the prominent gravity difference is caused by the density difference of about 0.1 g/㎤ between the Bulguksa Granite in the west and the Cretaceous Sandstone in the east side, (2) the fault core is elongated vertically into a depth of about 2,000 meters and extended horizontally 3,000 meters to the NNE direction from Naengsuri area. Our results present that the gravity field method is a very effective tool to estimate a three -dimensional image of the active fault core.