• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.687-693
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea. showed: (1) average RMSE error of the approach was 0.435 pixel; (2) the average number of matching points was over 25,573; (3) the average processing time was 4.2 min per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.47-54
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• 2008

In this paper, we propose an auto-detection algorithm of basal cell carcinoma(BCC) from the protophorphyrin IX(PpIX) fluorescence image induced by appling the methyl 5-aminolaevulinate(MAL) ointment-induced protophorphyrin IX(PpIX) to the skin tumour area and then shining the wood lamp on the area. The proposed algorithm first generates 3 mask areas-tumor area, suspected tumor area and tumor free area and then applies local watershed algorithm to the turner and the suspected tumor areas to make small watershed regions that include similar luminance value pixels. Next, small watershed regions are merged by hierarchical queue based fast region merging that uses the difference between the average luminance values of adjacent watershed regions as a region merging criterion and finally BCC regions are detected. 50 tissue samples are acquired from the tumour regions of 10 patients with BCC that are extracted by using the proposed algorithm and are performed pathological examination by expert dermatologist. Experiment result shows the rate of tumor detection from BCC lesion using presurgical in vivo of MAL-indeuced PpIX fluorescence has high sensitivity 94.1% and relatively high specificity 82.6%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.3-12
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• 2016

This paper describes the development of the ground penetrating radar (GPR) system using UWB impulse radar with 48 Channel array. GPR is an effective alternative technology to resolve th disadvantages of metal detectors. Metal detectors have a very low detection probability of non-metallic landmine and high false alarm rates caused by metallic materials under the ground. In this paper, we use the mono-cycle pulse waveform with about 600 ps pulse width to obtain high resolution landmine microwave images. In order to analyze performances of this system, we utilize indoor test facility that made up of rough sandy loam which representative Korean soil. The mimic landmine models of metal/non-metal and anti-tank/anti-personnel landmines buried in DMZ (demilitarized zone) of Korea are used to analyze the detection depth and the shape of the mines using microwave image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.31-40
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• 2003

Most face recognition systems are based on 2D images and applied in many applications. However, it is difficult to recognize a face when the pose varies severely. Therefore, head pose estimation is an inevitable procedure to improve recognition rate when a face is not frontal. In this paper, we propose a novel head pose estimation algorithm for 3D face recognition. Given the 3D range image of an unknown face as an input, we automatically extract facial feature points based on the face curvature. We propose an Error Compensated Singular Value Decomposition (EC-SVD) method based on the extracted facial feature points. We obtain the initial rotation angle based on the SVD method, and perform a refinement procedure to compensate for remained errors. The proposed algorithm is performed by exploiting the extracted facial features in the normaized 3D face space. In addition, we propose a 3D nearest neighbor classifier in order to select face candidates for 3D face recognition. From simulation results, we proved the efficiency and validity of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.11-23
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• 2011

In this paper, an accurate and fully automatic video object segmentation method is proposed for video conferencing systems in which the real-time performance is required. The proposed method consists of two steps: 1) accurate object extraction on the initial frame, 2) real-time object extraction from the next frame using the result of the first step. Object extraction on the initial frame starts with generating a cumulative edge map obtained from frame differences in the beginning. This is because we can estimate the initial shape of the foreground object from the cumulative motion. This estimated shape is used to assign the seeds for both object and background, which are needed for Graph-Cut segmentation. Once the foreground object is extracted by Graph-Cut segmentation, real-time object extraction is conducted using the extracted object and the double edge map obtained from the difference between two successive frames. Experimental results show that the proposed method is suitable for real-time processing even in VGA resolution videos contrary to previous methods, being a useful tool for immersive video conferencing systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.44-51
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• 2009

Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.408-408
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• 2023

하천공간은 하도, 사주, 식생, 하천구조물 등에 대한 특성을 지니고 있으며, 현장조사를 통해 하천공간에 대한 자료를 분석하여 기초자료를 생산한다. 기존에는 현장에서 육안조사나 지상에서 사진촬영, 스케치방법으로 하천공간특성에 대한 조사를 수행하였으나, 지상에서 조사한 자료은 하천특성에 대한 물리적·공간적 특성을 파악하기 어렵고 자료의 활용성이 낮은 한계점이 존재한다. 이와 같은 한계를 극복하기 위해 GIS 및 RS 기술을 활용한 고도화된 첨단조사 기술 및 장비가 도입되어 활용되고 있다. 본 연구에서는 하천공간특성을 GIS 기반으로 객체지향 분류 적용 연구와 분류 항목에 따른 공간분석 연구를 수행하였다. 연구를 위한 대상지역은 섬진강권역의 지석천 유역 하류부에 위치하고 있는 지석천 친수공원을 대상으로 선정하였다. 대상지역의 고해상도 항공영상을 수집 및 정합한 후 QGIS에서 제공하는 Orfeo ToolBox(OTB)의 LSMS(Large Scale Mean-Shift) 기법으로 정합한 항공영상의 객체지향 영상분할을 실시하여 벡터 레이어를 생성하였고, 하천공간특성에 따른 항목을 선정하여 각 항목의 영역에 대한 선별을 통해 훈련데이터를 생성하였다. 훈련데이터는 랜덤 포레스트를 이용하여 각 항목에 대한 자동 분류를 확인하였으며, 하천공간특성의 정량적 평가를 위해 분류된 각 항목별 공간분석을 통해 면적, 위치정보(위도, 경도, 표고)를 산정하였다. 분석 결과, 하천공간특성을 GIS 기반의 벡터 레이어와 각 항목에 대한 정량적 분석을 통해 하천공간의 DB를 구축하였다. 이와 같이 하천공간 DB 구축을 통해 전국 하천관리체계를 위한 기초자료를 구축하고자 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.374-376
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• 2021

For the diagnosis of cancer-related diseases in clinical practice, pathological examination using biopsy is essential after basic diagnosis using imaging equipment. In order to proceed with such a biopsy, the assistance of an oncologist, clinical pathologist, etc. with specialized knowledge and the minimum required time are essential for confirmation. In recent years, research related to the establishment of a system capable of automatic classification of cancer cells using artificial intelligence is being actively conducted. However, previous studies show limitations in the type and accuracy of cells based on a limited algorithm. In this study, we propose a method to identify a total of 4 cancer cells through a convolutional neural network, a kind of deep learning. The optical images obtained through cell culture were learned through EfficientNet after performing pre-processing such as identification of the location of cells and image segmentation using OpenCV. The model used various hyper parameters based on EfficientNet, and trained InceptionV3 to compare and analyze the performance. As a result, cells were classified with a high accuracy of 96.8%, and this analysis method is expected to be helpful in confirming cancer.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.85-92
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• 2023

In this paper, we propose a method for automatically generating story-based abbreviated summaries from long-form dramas and movies. From the shooting stage, the basic premise was to compose a frame with illusion of depth considering the golden division as well as focus on the object of interest to focus the viewer's attention in terms of content delivery. To consider how to extract the appropriate frames for this purpose, we utilized elemental techniques that have been utilized in previous work on scene and shot detection, as well as work on identifying focus-related blur. After converting the videos shared on YouTube to frame-by-frame, we divided them into a entire frame and three partial regions for feature extraction, and calculated the results of applying Laplacian operator and FFT to each region to choose the FFT with relative consistency and robustness. By comparing the calculated values for the entire frame with the calculated values for the three regions, the target frames were selected based on the condition that relatively sharp regions could be identified. Based on the selected results, the final frames were extracted by combining the results of an offline change point detection method to ensure the continuity of the frames within the shot, and an edit decision list was constructed to produce an abbreviated summary of 62.77% of the footage with F1-Score of 75.9%

• Journal of radiological science and technology
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• v.35 no.2
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• pp.133-139
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• 2012

For the examination of nuclear medicine, myocardial scan is a good method to evaluate a hemodynamic importance of coronary heart disease. but, the automatized qualitative measurement is additionally necessary to improve the decoding efficiency. we suggests the creation of cardiac three-dimensional model and model of three-dimensional cardiac thickness as a new measurement. For the experiment, cardiac reduced cross section was obtained from SPECT. Next, the pre-process was performed and image segmentation was fulfilled by level set. for the modeling of left cardiac thickness, it was realized by applying difference equation of two-dimensional laplace equation. As the result of experiment, it was successful to measure internal wall and external wall and three-dimensional modeling was realized by coordinate. and, with laplace formula, it was successful to develop the thickness of cardiac wall. through the three-dimensional model, defects were observed easily and position of lesion was grasped rapidly by the revolution of model. The model which was developed as the support index of decoding will provide decoding information to doctor additionally and reduce the rate of false diagnosis as well as play a great role for diagnosing IHD early.