• 융합신호처리학회논문지
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• 제6권1호
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• pp.33-38
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• 2005

데이터를 디지털화하거나 전송하는 과정에서 여러 가지 원인에 의해 열화가 발생하고 있으며, 이러한 열화의 주된 원인은 잡음이다. 따라서 잡음에 의해 훼손된 신호를 복원하기 위하여 웨이브렛을 이용한 방법들에 대한 연구가 활발히 진행되고 있다. 그리고 AWGN 환경에서 잡음을 제거하기 위한 가장 일반적인 연구 동향은 threshold에 기초한 방법들이다. 그러나 이러한 방법은 잡음에 대한 통계적인 특징만을 고려함에 따라 복원된 신호는 여전히 많은 잡음들을 포함한다. 따라서 본 논문에서는 웨이브렛 상세계수의 누적을 통한 새로운 신호 복원 방법을 제시하여, 신호의 edge 성분에 대한 복원과 잡음 제거 성능을 향상시켰다. 그리고 개선 효과의 판단 기준으로 SNR을 사용하였으며, 객관적인 판단을 위해 기존의 방법들과 비교하였다.

• 한국정보과학회논문지:시스템및이론
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• 제30권3_4호
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• pp.168-185
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• 2003

본 논문은 하나의 스케치 면도인 선화로부터 면 인식의 탐색 영역을 축소하고 다양한 3차원 물체를 빠르게 복원하는 점진적인 알고리즘을 제안한다. 복원 과정의 입력으로 사용되는 스케치 면도는 파선이 제거되지 않은 모서리-꼭지점 그래프인 2차원 스케치 면도로서 3차원 와이어프레임 물체의 부정확한 free-hand 스케치이다. 알고리즘은 두 단계로 수행된다. 면 인식 단계에서는 스케치 면도로부터 모든 가능 한 면을 생성하고 탐색 공간을 축소하기 위한 기하학적 위상학적 제약 조건을 이용하여 면을 불가능한 면, 기본 면, 최소 면으로 분류한다. 제안 알고리즘은 물체를 구성하는 실제 면을 빠르게 인식하기 위하여 최소 면만을 탐색한다 물체 생성 단계에서는 면의 스케치 순서에 따라 물체의 꼭지점 좌표를 최적화함으로써 3차원 구조를 점진적으로 계산한다. 점진적 방법은 복원 과정에서 물체와 스케치 도면 사이의 관계로부터 유도된 3차원 제약 조건을 적용함으로써 최적 3차원 물체를 빠르게 복원한다. 또한, 스케치 도중에 시점 이동을 허용한다. 점진적 복원 알고리즘을 기술하고 실제 구현 결과를 보인다.

• 한국광학회지
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• 제19권4호
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• pp.270-275
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• 2008

본 논문에서는 집적 영상의 획득과 복원을 통하여 장애물에 가려진 물체를 인식하는 기술은 제안하고 구현하였다. 집적 영상의 복원은 해당되는 화소 세기의1차 확률적 특성인 평균으로 구한다. 복원평면까지의 거리는 2차 확률적 특성인 표준 편차를 이용하여 구하고3차원 물체의 경계(edge)를 검출한다. 표준 편차의 합을 최소로 하는 거리에서 복원된 영상을 표적인식에 이용한다. 표적인식은 주성분 분석(principle component analysis, PCA) 분류기를 복원된 영상에 적용하였다. 표적 분류에 대한 판정은 분류기에 의해서 투영된 클래스의 평균 특징 벡터와 테스트 특징 벡터간의 유클리드 거리(Euclidean distance)를 이용한다. 실험 및 시뮬레이션을 통하여 가려진 표적을 본 논문에서 제안한 방법을 통하여 오차 없이 분류하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.304-309
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• 1999

In this paper, we presents a new disparity map refinement algorithm using statistical characteristics of disparity map and edge information. The proposed algorithm generate a refined disparity map using disparity maps which are obtained from area and feature-based Stereo Matching by selecting a disparity value of edge point based on the statistics of both disparity maps. Experimental results on aerial stereo image show the better results than conventional fusion algorithms in the disparity error. This algorithm can be applied to the reconstruction of building image from the high resolution remote sensing data.

• 한국정보처리학회논문지
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• 제7권7호
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• pp.2254-2263
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• 2000

This paper proposes an efficient predictive image-compression technique based on vector quantization of blocks of pels. In the proposed method edge contents of blocks control the selection of predictors and block shapes as well. The maximum number of bits assigned to quantizers has been in creased to 3bits/pel from 1/5bits/pel, the setting employed by forerunners in predictive vector quantization of images. This increase prevents the saturation in SNR observed in their results in high bit rates. The variable block shape is instrumental in eh reconstruction of edges. The adaptive procedure is controlled by means of he standard deviation ofp rediction errors generated by a default predictor; the standard deviation address a decision table which can be set up beforehand. eh proposed method is characterized by overall improvements in image quality over A-VQ-PE and A-DCT VQ, both of which are known for their efficient use of vector quantizers.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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• pp.113-116
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• 2000

Human face recognition and 3D human face reconstruction has been studied in this paper. To find the facial feature points, find edge from input image and analysis the accumulated histogram of edge information. This paper use a Generic Face Model to display the 3D human face model which was implement with OpenGL and generated with 500 polygons. For reality of 3D human face model, we propose Group matching mapping method between facial feature points and the one of Generic Face Model. The personalized 3D human face model which resembles real human face can be generated automatically in less than 5 seconds on Pentium PC.

• 대한기계학회논문집
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• 제19권11호
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• pp.3031-3040
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• 1995

Two-dimensional incompressible Navier-Stokes equations have been solved by the node-centered finite volume method with the unstructured triangular meshes. The pressure-velocity coupling is handled by the artificial compressibility algorithm due to its computational efficiency associated with the hyperbolic nature of the resulting equations. The convective fluxes are obtained by the Roe's flux difference splitting scheme using edge-based connectivities and higher-order differences are achieved by a reconstruction procedure. The time integration is based on an explicit four-stage Runge-Kutta scheme. Numerical procedures with local time stepping and implicit residual smoothing have been implemented to accelerate the convergence for the steady-state solutions. Comparisons with experimental data and other numerical results have proven accuracy and efficiency of the present unstructured approach.

• 전자공학회논문지S
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• 제35S권5호
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).

• Journal of Information Processing Systems
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• 제19권3호
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• pp.275-288
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• 2023

Many image edge details are easily lost in the image denoising process, and the smooth image regions are prone to produce jagged. In this paper, we propose a wavelet-based image global k- singular value decomposition variational method to remove image noise. A layer of wavelet decomposition is applied to the noisy image first. Then, the image global k-singular value decomposition (IGK-SVD) method is used to remove the random noise of low-frequency components. Furthermore, a constructed variational denoising method (VDM) removes the random noise in the high-frequency component. Finally, the denoised image is obtained by wavelet reconstruction. The experimental results show that the proposed method's peak signal-to-noise ratio (PSNR) value is higher than other methods, and its structural similarity (SSIM) value is closer to one, indicating that the proposed method can effectively suppress image noise while retaining more image edge details. The denoised image has better denoising effects.

• 전자공학회논문지B
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• 제31B권2호
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• pp.45-55
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• 1994

In this paper, a block based image approximation technique using the Self Affine System(SAS) from the fractal theory is suggested. Each block of an image is divided into 4 tiles and 4 affine mapping coefficients are found for each tile. To find the affine mapping cefficients that minimize the error between the affine transformed image block and the reconstructed image block, the matrix euation is solved by setting each partial differential coefficients to aero. And to ensure the convergence of coding block. 4 uniformly partitioned affine transformation is applied. Variable block size technique is employed in order to applynatural image reconstruction property of fractal image coding. Large blocks are used for encoding smooth backgrounds to yield high compression efficiency and texture and edge blocks are divided into smaller blocks to preserve the block detail. Affine mapping coefficinets are found for each block having 16$\times$16, 8$\times$8 or 4$\times$4 size. Each block is classified as shade, texture or edge. Average gray level is transmitted for shade bolcks, and coefficients are found for texture and edge blocks. Coefficients are quantized and only 16 bytes per block are transmitted. Using the proposed algorithm, the computational load increases linearly in proportion to image size. PSNR of 31.58dB is obtained as the result using 512$\times$512, 8 bits per pixel Lena image.