• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.26-31
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• 2010

We proposed a new directional edge based interpolation, DEBI, by combining two weighted directional information to reduce blurred edges and annoying artifacts. Four isotropic gradient masks are employed in defining edge directions and they are proven to hold a first order derivative relation with respect to a rotating coordinate. Two minimum gradients among four absolute directional results are shown to be sufficient to describe slant edges efficiently. Compared with widely used bilinear and bicubic interpolation methods, the proposed algorithm results in a noticeable improvement along edge area.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.501-509
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• 2011

In this paper, we propose an edge profile adaptive bi-directional diffusion interpolation method which consists of shock filter and level set. In recent years many interpolation methods have been proposed but all methods have some degrees of artifacts such as blurring and jaggies. To solve these problems, we adaptively apply shock filter and level set method where shock filter enhances edge along the normal direction and level set method removes jaggies artifact along the tangent direction. After the initial interpolation, weights of shock filter and level set are locally adjusted according to the edge profile. By adaptive coupling shock filter with level set method, the proposed method can remove jaggies artifact and enhance the edge. Experimental results show that the average PSNR and MSSIM of our method are increased, and contour smoothness and edge sharpness are also improved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.5
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• pp.620-627
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• 2003

Radiative heat transfer in an axisymmetric enclosure with absorbing, emitting, and scattering medium is studied here by using the different methods such as MDOM, FVM, and FVM2 with emphasis on the treatment of angular derivative term, which appears in a curvilinear coordinates due to angular redistribution. After final discretization equation for FVM2 is introduced by using the step scheme and directional weights, present approach is validated by applying it to three different benchmarking problems with absorbing, emitting, and scattering medium.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.193-204
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• 1997

In this research we propose the design of the Order Statistic-Adaptive Weighted Average Hybrid(OS-AWAH) filter which can suppress noise from the corrupted image sequence effectively while preserving the image structure. The proposed filter combines the desirable properties of the order static based spatial filter which can preserve the image structure while reducing noise and the adaptive weighted average based temporal filter which can adapt the filtering weights according to the amount of motion without motion estimation. Performance characteristics of the OS-AWAH filter in noisy sequences containing moving step edges are investigated throuth computer simulations and compared with the median based filters such as 3-D WM(weighted median) filter, MMF (multistage median filter), ADCWM(adaptive directional center weighted median) filter. The visual evaluations are also carried out by applyin gthe filters to the real images. The statistical analysis and experimental reslts show that the OS-AWAH filter is effective in preserving image structures while suppressing noise effectively without motion compensation preprocessing.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.95-99
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• 2018

In this paper, we propose an efficient pixel value prediction algorithm that can accurately predict pixel value using neighboring pixel values scanned in reverse s-order in the image. Generally, image has similarity with similar values between adjacent pixel values, and may have directional edge characteristics. In this paper, we proposed a method to improve pixel value prediction accuracy by improving GAP(Gradient Adjacent Pixel) algorithm for predicting pixel value by using similarity between adjacent pixels and edge characteristics. The proposed method increases the accuracy of the predicted pixel value by precisely predicting the pixel value using the positional weights of the neighboring pixels. Experiments on real images confirmed the superiority of the proposed algorithm. The proposed algorithm is useful for applications such as reversible data hiding, reversible watermarking, and data compression applications.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.9-18
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• 2016

This paper proposes a multi-view Wyner-Ziv Video coding scheme based on spatio-temporal adaptive estimation. The proposed algorithm is designed to search for a better estimated block with joint bi-directional motion estimation by introducing weights between temporal and spatial directions, and by classifying effectively the region of interest blocks, which is based on the edge detection and the synthesis, and by selecting the reference estimation block from the effective motion vector analysis. The proposed algorithm exploits the information of a single frame viewpoint and adjacent frame viewpoints, simultaneously and then generates adaptively side information in a variety of closure, and reflection regions to have a better performance. Through several simulations with multi-view video sequences, it is shown that the proposed algorithm performs visual quality improvement as well as bit-rate reduction, compared to the conventional methods.