• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2001.11b
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• pp.139-142
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• 2001

본 논문에서는 공간 영역의 제약 정보를 이용한 적응 영상 복원 방식을 제안한다. 공간 영역의 제약 정보로는 국부 정보의 평균, 분산 및 최대 값을 이용하였다 반복 기법을 이용하여 매 반복 해에서 얻어진 복원 영상으로부터 상기 제약 정보를 설정하게 되고, 위의 제약 정보는 임의의 입력 값에 의해 정의되는 매개 변수와 더불어 복원 영상의 국부 완화 정도를 결정하게 된다. 제안된 방식을 이용하여 복원 영상을 얻기 위해 비적응 복원 방식보다 빠른 수렴 속도를 갖게 됨을 알 수 있었다. 또한, 국부적으로 제어된 완화 정도를 지닌 복원 영상을 얻을 수 있었다.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.649-652
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• 2001

본 논문에서는 공간 영역의 제약 정보를 이용한 적응 영상 복원 방식을 제안한다. 공간 영역의 제약 정보로는 국부 정보의 평균, 분산 및 최대 값을 이용하였다. 반복 기법을 이용하여 매 반복 해에서 얻어진 복원 영상으로부터 상기 제약 정보를 설정하게 되고, 위의 제약 정보는 임의의 입력 값에 의해 정의되는 매개 변수와 더불어 복원 영상의 국부 완화 정도를 결정하게 된다. 제안된 방식을 이용하여 복원 영상을 얻기 위해 비적응 복원 방식보다 빠른 수렴 속도를 갖게 됨을 알 수 있었다. 또한, 국부적으로 제어된 완화 정도를 지닌 복원 영상을 얻을 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.975-981
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• 2007

In this paper, we propose a spatially adaptive image restoration algorithm using local and statistics and estimated noise. The ratio of local mean, variance, and maximum values with different window size is used to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. In addition, the additive noise estimated from partially restored image and the local constraints are used to determine a parameter for controlling the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained without a prior knowledge about the noise. Experimental results demonstrate that the proposed algorithm requires the similar iteration number to converge, but there is the improvement of SNR more than 0.2 dB comparing to the previous approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.232-238
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• 2003

In this paper, we propose a spatially adaptive image restoration algorithm using local statistics. The local mean, variance, and maximum values are utilized to constrain the solution space, and these parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared to the non-adaptive algorithm. In addition, a smooth solution with a controlled degree of smoothness is obtained. Experimental results demonstrate the capability of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.743-747
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• 2010

This paper presents an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum value are used to constrain the solution space. These parameters are computed at each iteration step using partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least squares (RCLS) approach.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.481-488
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.1
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• pp.164-169
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• 2003

In this paper, we investigate recognition of human faces in a meeting room. The major challenges of identifying human faces in this environment include low quality of input images, poor illumination, unrestricted head poses and continuously changing facial expressions and occlusion. In order to address these problems we propose a novel algorithm, Dynamic Space Warping (DSW). The basic idea of the algorithm is to combine local features under certain spatial constraints. We compare DSW with the eigenface approach on data collected from various meetings. We have tested both front and profile face images and images with two stages of occlusion. As a result from the experiment, we obtained 82.7% for PCA algotithm, and 89.4% for DSW. We get to obtain 6.9% better result from conductive DSW approach rather than PCA. It turned out to be that it shows more original and unique facial image.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.67-80
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• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.110-121
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• 2009

In this paper, we propose the new method that offer detailed-information through relax the system memory limitation about 3D model to user. That method based on making LOD(Level of Detail) model from huge 3D data of structure cultural assets. In our method as transformed AOSP algorithm, first of all it create the hierarchical structure space about 3D data, and create the LOD model by surface simplification. Then it extract the ROI(Region of Interest) of user in simplified LOD model, and then do rendering by original model and same surface detailed-information after process the local detailed in extracted region. To evaluate the proposed method, we have some experiment by using the precise 3D scan data of structure cultural assets. Our method can offer the detailed-information same as exist method, and moreover 45% reduced consumption of memory experimentally by forming mesh structure same as ROI of simplified LOD model. So we can check the huge structure cultural assets particularly in general computer environment.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.355-362
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• 2010

Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.