• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.382-384
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• 2006

본 논문에서는 저해상도 한글 영상을 자소 단위로 분리하는 방법을 제안한다. 비디오 자막이나 저해상도 스캔 영상의 경우 자소간 획이 접촉되거나 잡영이 많이 포함되어 기존의 자소 분할 방법으로는 한계가 있다. 한자 문자열을 문자 단위로 분할하는데 사용된 비선형 분할 경로 알고리즘을 한글 낱자 영상에 적용하여 자소 단위로 분할한다. 기존의 분할 경로 알고리즘을 한글 자소 분할에 효과적으로 적용하기 위해서 우세점 탐지 알고리즘을 이용하여 자소간 접촉점을 찾고 이를 바탕으로 생성된 분할 경로에 따라 여러 개의 자소 후보 영상이 생성된다. 자소 영상을 자소 인식기로 인식한 결과 높은 인식률을 보이는 것을 실험을 통하여 확인하였다.

• Journal of IKEEE
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• v.25 no.1
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• pp.139-147
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• 2021

This paper introduces an image decomposition-based deep learning method and loss function to improve low-light images. In order to remove color distortion and halo artifact, illuminance channel of an input image is decomposed into reflectance and luminance channels, and a decomposition-based multiple structural deep learning process is applied to each channel. In addition, a mixed norm-based loss function is described to increase the stability and remove blurring in reconstructed image. Experimental results show that the proposed method effectively improve various low-light images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.240-248
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• 2002

This paper presents a new motion-based segmentation algorithm of moving objects in image sequences. The procedure toward complete segmentation consists of two steps: pixel labeling and motion segmentation. In the first step, we assign a label to each pixel according to magnitude of velocity vector. And velocity vector is generated by optical flow. And, in the second step, we have modeled motion field as a markov random field for noise canceling and make a segmentation of motion through energy minimization. We have demonstrated the efficiency of the presented method through experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.1-10
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• 2003

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.260-267
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• 2011

This paper proposes a method for segmenting objects from the background in IR(Infrared) images based on GrabCut algorithm. The GrabCut algorithm needs the window encompassing the interesting known object. This procedure is processed by user. However, to apply it for object recognition problems in image sequences. the location of window should be determined automatically. For this, we adopted the Otsu' algorithm for segmenting the interesting but unknown objects in an image coarsely. After applying the Otsu' algorithm, the window is located automatically by blob analysis. The GrabCut algorithm needs the probability distributions of both the candidate object region and the background region surrounding closely the object for estimating the Gaussian mixture models(GMMs) of the object and the background. The probability distribution of the background is computed from the background window, which has the same number of pixels within the candidate object region. Experiments for various IR images show that the proposed method is proper to segment out the interesting object in IR image sequences. To evaluate performance of proposed segmentation method, we compare other segmentation methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.119-127
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• 2000

금속판/형과스크린 계측기와 CCD 카메라를 이용한 방사선영상장치가 현재 전자포탈영상에 널리 쓰이고 있다. 이 장치의 효율적인 영상획득을 위해 계측효율이 좋고, 공간분해능력이 뛰어난 금속판/ 형과스크린 계측기의 두께를 최적화할 필요가 있었다. 이 논문에서는 금속판과 형광스크린의 두께가 계측효율과 공간분해능에 미치는 영향이 연구되었다. 이 결과는 치료 엑스선 영상장치에 쓰일 수 있는 금속판/형과스크린 계측기의 최적화된 두께를 결정하는데 쓰일 수 있다. 몬테칼로 방법을 이용하여 계산한6 MV 선형가속기에서 발생되는 엑시선의 에너지 스펙트럼을 바탕으로, 여러 가지 두께의 금속판/형광스크린에 대하여 계측효율과 공간분해능을 계산하였고, 이를 실험을 통해 검증하였다. 계측효율은 입사된 엑스선의 에너지가 형광스크린에 흡수된 비율로 계산되며, 공간분해능은 흡수된 에너지의 공간 분포를 통해 계산되었다. 계측효율은 금속판의 두께에 의해, 공간분해능은 형광스크린의 두께에 의해 결정될 수 있음을 본 연구를 통해 확인할 수 있었고, 이로써 특정이용에 관련된 금속판/형광스크린의 두께에 대한 서로 보상 (trade-off) 관계에 있음을 계산과 측정결과를 통해 확인할 수 있었고, 이로써 특정이용에 관련된 금속판/형광스크린 계측기의 최적화된 두께를 산출할 수 있게 되었다. 계산을 바탕으로 CCD를 이용한 전자포탈영상장치의 시작품을 설계 및 제작하였고 팬텀을 이용하여 영상을 얻었다. 단일 프레임 영상은 노이즈가 많으나, 프레임 평균 방법을 이용하여 영상의 질을 향상시킬 수 있었다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.333-336
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• 2001

본 논문에서는 영상 시퀀스 상에서 물체의 가려짐을 고려하여 상대적인 깊이 순서에 의해 정렬되는 계층을 분리하기 위한 새로운 움직임 분할 방법을 제안한다. 블록을 기반으로 한 움직임 추정 및 클러스터링 과정을 통하여 각 계층에 대한 블록영역을 구하고, 이 블록영역에 대하여 윤곽선 추출을 이용하여 각 계층에 대한 정확한 객체를 분리할 수 있다. 이러한 움직임 분할방법을 통한 동영상의 계층적인 표현은 영상에서 원하지 않는 물체, 전경, 배경의 제거나 기존의 영상을 이용한 새로운 영상의 합성에 이용될 수 있으며, 분할을 통해 얻어진 객체는 영상 압축, 영상 합성 등을 위한 데이터베이스에 저장되어 응용될 수 있다.

• Korean Journal of Optics and Photonics
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• v.14 no.6
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• pp.636-642
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• 2003

We proposed the optical system using two divided halftone images to hide the original image and a joint transform correlator. The encryption procedure is performed by the Fourier transform of the product of each divided image by visual cryptography and the same random image which is generated by computer processing. As a result, we can obtain two Fourier divided images which are used as the encrypted image and the decrypting key, respectively. In the decryption procedure, both the encrypted image and the decrypting key are located on the joint input plane. Then the original image is reconstructed on a CCD camera which is located in the output plane. An autocorrelation term of joint transform correlator contributes to decrypt the original image. To demonstrate the efficiency of the proposed system, computer simulations and noise analysis are performed. The result show that the proposed system is a very useful optical certification system.

• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.146-155
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• 1997

The new video coding standard Iv1PEG-4 is enabling content-based functionalities. It requires a prior decomposition of sequences into video object planes (VOP's) so that each VOP represents moving objets. This paper addresses an image segmentation method for separating moving objects from still background (non-moving area) in video sequences using a statistical hypothesis test. In the proposed method. three consecutive image frames are exploited and a hypothesis testing is performed by comparing two means from two consecutive difference images. which results in a T-test. This hypothesis test yields a change detection mask that indicates moving areas (foreground) and non-moving areas (background), Moreover. an effective method for extracting