• Radiation Oncology Journal
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• 제35권2호
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• 경영과정보연구
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• 제1권
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• pp.401-412
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers have been devoted their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new bands selection method for multispectral bands of remote sensed image data that use rough set theory. Using indiscernibility relations in rough sets, we show that can select the efficient bands of multispectral image data, automatically.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.205-208
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers devote their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new classification method for remote sensed image data that use rough set theory. Using indiscernibility relation of rough sets, we show that can classify image data very easily.

• 대한원격탐사학회지
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• 제24권3호
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• pp.251-256
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• 2008

Morphing method, one of classic image processing algorithms, has been used in various application fields. The motivation of this work is to investigate its applicability in consideration to geo-spatial data including airborne or space-borne images. For this purpose, the Beier and Neely morphing algorithm is tentatively implemented in the form of a prototype with user interface. As the results, this feature-based morphing with paired image sets can be used for general users: image simulation using two or more images and construction of color-blending image between source image and destination image in different types. Some simple application cases were demonstrated. This scheme is the simple and useful approach for those who want to utilize both geo-spatial data sets and airborne/space-borne image sets.

• 대한원격탐사학회지
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• 제10권2호
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• pp.49-62
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• 1994

Look direction bias in a single look SAR image can often be misinterpreted in the geological application of radar data. This paper investigates digital processing techniques for SAR image data integration and compensation of the SAR data look direction bias. The two important approaches for reducing look direction bias and integration of multiple SAR data sets are (1) principal component analysis (PCA), and (2) wavelet transform(WT) integration techniques. These two methods were investigated and tested with the ERS-1 (VV-polarization) and CCRS*s airborne (HH-polarization) C-SAR image data sets recorded over the Sudbury test site, Canada. The PCA technique has been very effective for integration of more than two layers of digital image data. When there only two sets of SAR data are available, the PCA thchnique requires at least one more set of auxiliary data for proper rendition of the fine surface features. The WT processing approach of SAR data integration utilizes the property which decomposes images into approximated image ( low frequencies) characterizing the spatially large and relatively distinct structures, and detailed image (high frequencies) in which the information on detailed fine structures are preserved. The test results with the ERS-1and CCRS*s C-SAR data indicate that the new WT approach is more efficient and robust in enhancibng the fine details of the multiple SAR images than the PCA approach.

• 한국지능시스템학회논문지
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• 제8권3호
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• pp.16-25
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• 1998

본 논문에서는 초 다중 밴드 환경의 효과적인 데이터 분류를 위해서 Roungh 집합 이론을 이용한 특징 추출 방법을 제안한다. 다중 분광 이미지 데이터의 특성을 분석하고, 그 분석 결과를 토대로 Rough집합이론의 식별 능력을 이용하여 가장 효과적인 밴드를 선택할 수 있도록 한다. 실험으로는 Landsat TM으로부터 취득한 데이터에 적용시켰으며, 이를 통해 전통적인 밴드 특성에 의한 밴드 선택 방법과 본 논문에서 제안하는 러프 집합 이론을 이용한 밴드 선택 방법이 일치됨을 보이고 이를 통해 초다중 밴드 환경에서의 특징 추출에 대한 이론적 근거를 제시한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.314-319
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• 1998

In this paper, for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theroy is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band usin indiscernibility relation of Rough sets theory from analysis results. Proposed method is applied to LAMDSAT TM data on 2, June, 1992. Among them, normal distributive data were experimented, mainly. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• Journal of Advanced Marine Engineering and Technology
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• 제34권3호
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• pp.407-412
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• 2010

According to the remarkable advances in sonar technology, positioning capabilities and computer processing power we can accurately image and explore the seafloor in hydrography. Especially, Multibeam Echo Sounder can provide nearly perfect coverage of the seafloor with high resolution. Since the mid-1990's, Multibeam Echo Sounders have been used for hydrographic surveying in Korea. In this study, new marine data set as an effective decision-making tool in various fields was proposed by visualizing and combining with Multibeam sonar data and marine spatial data sets such as satellite image and digital nautical chart. The proposed method was tested around the port of PyeongTaek-DangJin in the west coast of Korea. The Visualization and fusion methods are described with various marine data sets with processing. We demonstrated that new data set in marine GIS is useful in safe navigation and port management as an efficient decision-making tool.

• 경영과정보연구
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• 제3권
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.