• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.22-26
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• 1987

Texture provides an important acurce of information about the threedicensfornarry information of visible surface particulary for stationary conccular views. To recover three dicmensinoary information, the distorging effects of pro jection must be distinguished from properties of the texture on which the distrortion acts. In this paper, we show an approximated maximum likelihood estimation method by which we find surface oriemtation of the visible surface in gaussian sphere using local analysis of the texture, In addition assuming that an orthographic projection and a circle is an image formation system and a texel(texture element)respectively we derive the surface orientation from the distribution of variation by means of orthographic pro jemction of a tangent directon which exstis regulary in the are length of a circle we present the orientation parameters of textured surface with saint and tilt and also the surface normal of the resvlted surface orimentation as needle map. This algorithm was applied to geograghic contour and synthetic textures.

• Bulletin of the Korean Mathematical Society
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• v.50 no.4
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• pp.1061-1067
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• 2013

In this paper, we study surfaces of revolution without parabolic points in 3-Euclidean space $\mathbb{R}^3$, satisfying the condition ${\Delta}^{II}G=f(G+C)$, where ${\Delta}^{II}$ is the Laplace operator with respect to the second fundamental form, $f$ is a smooth function on the surface and C is a constant vector. Our main results state that surfaces of revolution without parabolic points in $\mathbb{R}^3$ which satisfy the condition ${\Delta}^{II}G=fG$, coincide with surfaces of revolution with non-zero constant Gaussian curvature.

• International Journal of Contents
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• v.3 no.2
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• pp.25-29
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• 2007

In this paper, we discuss the mesh segmentation problem which divides a given 3D mesh into several disjoint sets. To solve the problem, we propose a greedy method based on the merging priority metric defined for representing the geometric properties of meaningful parts. The proposed priority metric is a weighted function using five geometric parameters, those are, a distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. In special, we can control by setting up the weight values of the proposed geometric parameters to obtain visually better mesh segmentation. Finally, we carry out an experiment on several 3D mesh models using the proposed methods and visualize the results.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.126-129
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel type s as states of molecules in a lattice-like physical system defined on a GRF. Because of the MRFGRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular mteractions. The proposed adaptive iterative method was evaluated using simulation data generated by the Monte Carlo method. In the extensive experiments of this study, the proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• Journal of information and communication convergence engineering
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• v.15 no.4
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• pp.250-255
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• 2017

In this paper, we propose a method of detecting the region for measuring intima-media thickness (IMT). The existing methods for IMT measurement are automatic, but the region used for measuring IMT is not detected automatically but often set by the user. Therefore, research on detecting the intima-media region is needed for fully automated IMT measurement. The proposed method uses a morphological feature of the carotid artery visible as two long high-brightness horizontal lines at the upper and lower parts. It uses Gaussian blurring, ends-in search stretching, color quantization using a color-importance-based self-organizing map, and morphological operations to emphasize and to detect the morphological feature. The experimental results for evaluating the performance of the proposed method showed a 97.25% (106/109) success rate. Therefore, the proposed method can be used to develop a fully automated IMT measurement system.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.539-544
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• 2008

이 논문에서 크기의 변화에 강인한 인공물 축출 알고리듬을 제안한다. 인공물은 크기 및 카메라 센서의 특성에 따라 영상에 다양한 크기로 나타난다. 이 논문은 이러한 크기 변화에 강인한 인공물 축출 방법을 제안한다. 우선 LoG(Laplacian of Gaussian)를 이용하여 최적의 크기를 찾아낸다. 이를 이용하여 우리는 이웃한 정보를 포함할 수 있는 MAP-MRF(Maximum A Posterior-Markov Random Field) 레이블링(Labeling) 방법을 기반으로 인공물 축출을 위한 비용함수를 제안하였다. 인공물은 서로 근처에 존재하기 때문이다. 여기서 정보 비용함수(Data cost function)는 방향 히스토그램(Orientation histogram)을 이용하여 정의하였고, 스무딩 비용함수(Smoothing cost function)는 ICM(Iterated Conditional Modes)을 이용하여 정의한다. 최종적으로 이 알고리듬을 위성영상에 적용하여 알고리듬의 성능을 증명한다.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.270-273
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• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.93-97
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• 2008

In this letter, a novel wavelet-based semi-fragile watermarking scheme is presented which exploiting the time-frequency feature of chaotic map. We also analyze the robustness to mild modification and fragility to malicious attack of our scheme. Its application includes tamper detection, image verification and copyright protection of multimedia content. Simulation results show the scheme can detect and localize malicious attacks with high peak signal-to-noise ratio(PSNR), while tolerating certain degree of JPEG compression and channel additive white Gaussian noise(AWGN)

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.