• Title/Summary/Keyword: 외곽선 복원

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Edge Restoration in Blurred Image using 1/4 Selective Filter (1/4 선택 필터를 이용한 번짐 영상의 외곽선 복원)

  • Jeong, Woo-Jin;Lee, Jong-Min;Kim, Chaeyoung;Moon, Young-Shik
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.1
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    • pp.103-110
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    • 2015
  • In this paper, we propose a deblurring method using 1/4 selective filter. Deblurring methods require a lot of processing time for deblurring. In order to enhance execution speed, we propose a novel 1/4 selective filter. The proposed 1/4 selective filter restores major edge, but it distorts minor edge and texture. To solve this problem, we apply 1/4 selective filter to restore major edge and DOG(Difference of Gaussian) filter to restore minor edge and texture. Experimental results show that the proposed method removes the blur effectively.

Boundary Matching of Color and Depth Images Using Normalized Cross Correlation (정규화된 상호 연관성을 이용한 컬러 영상과 깊이 영상의 외곽선 매칭)

  • Yun, TaeHui;Sim, Jae-Young
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.45-46
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    • 2013
  • 본 논문에서는 깊이 영상과 컬러 영상의 매칭을 통한 강인한 전경 객체 영역화 기법을 제안한다. 기존의 컬러 영상 기반 객체 영역화 알고리즘은 배경과 객체의 색상이 유사한 경우 정확한 객체 영역화가 어렵다. 깊이 영상을 이용하면 이러한 오 검출을 줄일 수 있지만, 깊이 영상 취득 장비의 오류로 인하여 검출되는 객체 외곽선이 컬러 영상에 비해 세밀하지 못한 단점이 있다. 따라서, 깊이 영상의 외곽선을 비교적 세밀한 컬러 영상의 외곽선에 매칭시킨다. 아울러, 서로 다른 센서에서 취득한 두 영상을 매칭하기 위하여, 정규화된 상호연관성(normalized cross correlation)을 유사도 척도로 사용한다. 실험을 통하여 제안하는 알고리즘이 전경 객체 영역화의 오 검출을 줄이며, 동시에 객체 외곽선을 충실히 복원함을 확인한다.

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Shape Based Image Retrieval using Fourier Series (퓨리에 시리즈를 사용한 외형기반 이미지 검색)

  • Tak, Yoon-Sik;Hwang, Een-Jun
    • Proceedings of the Korean Information Science Society Conference
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    • pp.366-371
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    • 2006
  • 퓨리에 시리즈를 사용하면 이미지의 외곽선 특성을 표현할 수 있다. 이미지의 퓨리에 계수를 추출하기 위해서는 우선 이미지를 구성하는 주요 오브젝트를 표현하는 곡선을 추출한다. 이러한 곡선은 오브젝트의 특정 중심점에서 외곽선을 따라 일회전하면서 그 거리를 좌표상에 표시함으로써 얻을 수 있다. 기존의 퓨리에 계수를 추출하는 방법들은 추출된 계수를 이용하여 해당 곡선을 복원했을 때 원래의 곡선에 존재하던 상세한 특성을 표현하지 못한다는 단점이 있으며 이는 결국 이미지로부터 추출한 곡선을 사용하여 이미지를 검색할 때 정확도를 상당히 떨어뜨리게 한다. 이러한 문제점을 해결하기 위해서 본 논문에서는 Binary Range Reduction (BRR) 알고리즘을 제안한다. BRR 알고리즘은 원래의 곡선과 퓨리에 계수를 통해서 복원된 곡선간의 차이를 줄이기 위해서 전체의 곡선을 통해서 하나의 퓨리에 계수 세트를 추출하지 않고, 복원된 곡선이 원래의 곡선과 차이가 일정 크기 이상 나지 않도록 퓨리에 계수를 추출하는 구간을 나누어가며 퓨리에 계수를 추출한다. 이렇게 추출된 다수의 퓨리에 계수 세트를 통해서 복원된 곡선을 사용하여 이미지들 간의 유사도를 비교한다. 실험을 통하여 BRR 알고리즘을 사용하여 곡선에서 추출한 퓨리에 계수로 복원한 곡선이 원래 곡선의 특성을 정확하게 표현하고 있음을 보였고, 퓨리에 계수와 BRR알고리즘을 이미지 검색에 적용하였을 때, 높은 검색 결과를 얻을 수 있음을 보였다.

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3D Shape Reconstruction using the Focus Estimator Value from Multi-Focus Cell Images (다초점 세포 영상으로부터 추정된 초점 값을 이용한 3차원 형태 복원)

  • Choi, Yea-Jun;Lee, Dong-Woo;Kim, Myoung-Hee;Choi, Soo-Mi
    • Journal of the Korea Computer Graphics Society
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    • v.23 no.4
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    • pp.31-40
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    • 2017
  • As 3D cell culture has recently become possible, it has been able to observe a 3D shape of cell and volume. Generally, 3D information of a cell should be observed with a special microscope such as a confocal microscope or an electron microscope. However, a confocal microscope is more expensive than a conventional microscope and takes longer time to capture images. Therefore, there is a need for a method that can reconstruct the 3D shape of cells using a common microscope. In this paper, we propose a method of reconstructing 3D cells using the focus estimator value from multi-focal fluorescence images of cells. Initially, 3D cultured cells are captured with an optical microscope by changing the focus. Then the approximate position of the cells is assigned as ROI (Region Of Interest) using the circular Hough transform in the images. The MSBF (Modified Sliding Band Filter) is applied to the obtained ROI to extract the outlines of the cell clusters, and the focus estimator values are computed based on the extracted outlines. Using the computed focus estimator values and the numerical aperture (NA) of the microscope, we extract the outline of the cell cluster considering the depth and reconstruct the cells into 3D based on the extracted outline. The reconstruction results are examined by comparing with the combined in-focus portions of the cell images.

Implementation of Neural Filter Optimal Algorithms for Image Restoration (영상복원용 신경회로망 필터의 최적화 알고리즘 구현)

  • Lee, Bae-Ho;Mun, Byeong-Jin
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.7
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    • pp.1980-1987
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    • 1999
  • Restored image is always lower quality than original one due to distortion and noise. The purpose of image restoration is to improve the image quality by fixing the noise or distortion information. One category of spatial filters for image restoration is linear filter. This filter algorithm is easily implemented and can be suppressed the Gaussian noise effectively, but not so good performance for spot or impulse noise. In this paper, we propose the nonlinear spatial filter algorithm for image restoration called the optimal adaptive multistage filter(OAMF). The OAMF is used to reduce the filtering time, increases the noise suppression ratio and preserves the edge information. The OAMF optimizes the adaptive multistage filter(AMF) by using weight learning algorithm of back-propagation learning algorithm. Simulation results of this filter algorithm are presented and discussed.

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Segment-based Foreground Extraction Dedicated to 3D Reconstruction (3차원 복원을 위한 세그멘트 기반의 전경물체 추출)

  • Kim, Jeong-Hwan;Park, An-Jin;Jeong, Gi-Cheol
    • 한국HCI학회:학술대회논문집
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    • pp.625-630
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    • 2009
  • Researches of image-based 3D reconstruction have recently produced a number of good results, but they assumed that the accurate foreground to be reconstructed is already extracted from each input image. This paper proposes a novel approach to extract more accurate foregrounds by iteratively performing foreground extraction and 3D reconstruction in a manner similar to an EM algorithm on regions segmented in an initial stage, called segments. Here, the segments should preserve foreground boundaries to compensate for the boundary errors generated by visual hull, simple 3D reconstruction to minimize the computational time, and should also be composed of the small number of sets to minimize the user input. Therefore, we utilize image segmentation using the graph-cuts method, which minimizes energy function composed of data and smoothness terms, and the two methods are iteratively performed until the energy function is optimized. In the experiments, more accurate results of the foreground, especially in boundaries, were obtained, although the proposed method used a simple 3D reconstruction method.

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East Reconstruction of 3D Human Model from Contour Lines (외곽선을 이용한 고속 3차원 인체모델 재구성)

  • Shin Byeong-Seok;Roh Sung;Jung Hoe-Sang;Chung Min Suk;Lee Yong Sook
    • Journal of Biomedical Engineering Research
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    • v.25 no.6
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    • pp.537-543
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    • 2004
  • In order to create three-dimensional model for human body, a method that reconstructs geometric models from contour lines on cross-section images is commonly used. We can get a set of contour lines by acquiring CT or MR images and segmenting anatomical structures. Previously proposed method divides entire contour line into simply matched regions and clefts. Since long processing time is required for reconstructing cleft regions, its performance might be degraded when manipulating complex data such as cross-sections for human body. In this paper, we propose a fast reconstruction method. It generates a triangle strip with single tiling operation for simple region that does not contain branch structures. If there exist branches in contour lines, it partitions the contour line into several sub-contours by considering the number of vertices and their spatial distribution. We implemented an automatic surface reconstruction system by using our method which reconstructs three-dimensional models for anatomical structures.

Uniform Motion Deblurring using Shock Filter and Convolutional Neural Network (쇼크 필터와 합성곱 신경망 기반의 균일 모션 디블러링 기법)

  • Jeong, Minso;Jeong, Jechang
    • Journal of Broadcast Engineering
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    • v.23 no.4
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    • pp.484-494
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    • 2018
  • The uniform motion blur removing algorithm of Cho et al. has the problem that the edge region of the image cannot be restored clearly. We propose the effective algorithm to overcome this problem by using shock filter that reconstructs a blurred step signal into a sharp edge, and convolutional neural network (CNN) that learns by extracting features from the image. Then uniform motion blur kernel is estimated from the latent sharp image to remove blur in the image. The proposed algorithm improved the disadvantages of the conventional algorithm by reconstructing the latent sharp image using shock filter and CNN. Through the experimental results, it was confirmed that the proposed algorithm shows excellent reconstruction performance in objective and subjective image quality than the conventional algorithm.

Design and Implementation of Optimal Adaptive Generalized Stack Filter for Image Restoration Using Neural Networks (신경회로망을 이용한 영상복원용 적응형 일반스택 최적화 필터의 설계 및 구현)

  • Moon, Byoung-Jin;Kim, Kwang-Hee;Lee, Bae-Ho
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.36S no.7
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    • pp.81-89
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    • 1999
  • Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

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Real-Time Moving Object extraction for Superimposition (영상 합성을 위한 실시간 움직임 물체 추출)

  • 김종수;현대환;장성갑;최종수
    • Proceedings of the IEEK Conference
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    • pp.367-370
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    • 2001
  • 본 논문에서는 영상 합성을 위해 실시간으로 움직임 물체를 추출하고 움직임 물체의 홀이나 외곽선의 손실을 최소화하고 복원하는 알고리즘을 제안한다. 움직임은 기준영상과 현재 입력된 영상의 차이를 계산함으로써 검출 된다. 따라서 여기서 적당한 기준 영상과 검출 문턱치 방법의 선택이 필요하게 된다. 몇 가지 문턱치 선택 방법들이 연구되었나, 본 논문에서는 회귀적인 문턱치들을 이용한다. 그레이 영상을 통해 구해진 영역에서 칼라 값의 비교를 통해 손실된 영역을 복구하고 최종 실루엣 영상을 얻는다. 얻어진 움직임 물체의 실루엣 영상은 영상 합성에 이용한다.

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