• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.29-43
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• 1991

The study was performed to extraction the ground water potential area using LANDSAT TM data. The image processing techniques developed for the study are contrast transformation, differential filtering and pseudo stereoscopic image methods. These were examined for lineament extraction, lineament interpretation and the integration of vertor data with LANDSAT data. The differential filtering method is much usefull for lineament extraction, and all direction lineaments are clearly shown on the band 5 image of LANDSAT TM. The pseudo stereoscopic image are made in which color differential method is adopted, the pair images are usefull for the lineament interpretation. The results of the analysis are as follows. 1) there is a close correlation between lineament and cased well in the study area, because 33 wells of the developed 45 cased wells coincide with the lineaments. 2) 21 sites in the study area were selected for pumping test, and as a result 11 sites of them produces over than 200 ton/day.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.169-176
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• 2018

In this study, we propose a new Fire Flame Region Extraction using Block Homogeneity Segmentation method of the Fire Image with irregular texture and various colors. It is generally assumed that fire flame extraction plays a very important role. The Color Image with fire flame is divided into blocks and edge strength for each block is computed by using modified color histogram intersection method that has been developed to differentiate object boundaries from irregular texture boundaries effectively. The block homogeneity is designed to have the higher value in the center of region with the homeogenous colors or texture while to have lower value near region boundaries. The image represented by the block homogeneity is gray scale image and watershed transformation technique is used to generate closed boundary for each region. As the watershed transform generally results in over-segmentation, region merging based on common boundary strength is followed. The proposed method can be applied quickly and effectively to the initial response of fire.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.455-458
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• 2010

Extracting the brain from magnetic resonance imaging head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool produces a brain mask which may be too smooth for practical use to reduce the accuracy. This paper presents a novel and indirect brain extraction method based on non-brain tissue segmentation. Based on ITK, the proposed method allows a non-brain contour by using region growing to match with the original image naturally and extract the brain tissue. Experiments on two set of MRI data and 2D brain image in horizontal plane and 3D brain model indicate successful extraction of brain tissue from a head.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.79-82
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• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.81-86
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• 2003

In this paper, we propose the tracking method of moving object which use extracted object by difference between background image and target image in fixed domain. As a extraction method of object, calculate not pixel of full image but predefined some edge pixel of image to get a position of new object. Since the center area Is excluded from calculation, the extraction time is efficiently reduced. To extract object in the predefined area, get a starting point in advance and then extract size of width and height of object. Central coordinate is used to track moved object.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.153-156
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• 2000

Recently, there has been studied on feature extraction method for efficient content-based image retrieval. Especially, Many researchers have been studying on extracting feature from color Information, because of its advantages. This paper proposes a feature and its extraction method based on color correlogram that is extracted from color information in an image. the proposed method is computed from the image segmented into two parts; the complex part and the plain part. Our experiments show that the performance of the proposed method is better as compared with that of the original color correlogram method.