• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• Journal of Fashion Business
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• v.19 no.6
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• pp.14-27
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• 2015

This study explores the comparison of body image, body satisfaction, and clothing behaviors between Korean and British young women. Body image was measured by two methods: visual and verbal. For the data analysis, the Statistical Package for Social Science (SPSS) Version 16.0 for Windows was used to provide descriptive statistics, an independent sample t-test, and paired sample t- tests were applied in this study. Our results show that Korean and British female college students perceived ideal-body images that were smaller than their self defined body images. The ideal and self-images were significantly different in both groups. Both groups were dissatisfied with their own body size. The study was limited to a small sample size. Future studies using more participants from a more diverse age group and ethnic groups are recommended. The study will help marketers and retailers develop new products and new markets aimed at Korean and British women related to body image and body satisfaction.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.385-399
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• 2007

In many image processing applications, a random noise makes some trouble since most video enhancement functions produce visual artifacts if a priori of the noise is incorrect. The basic difficulty is that the noise and the signal are difficult to be distinguished. Typical unsharp masking (UM) enhances the visual appearances of images, but it also amplifies the noise components of the image. Hence, the applications of a UM are limited when noises are presented. This paper proposed statistical algorithms based on parametric and nonparametric tests to adaptively enhance the image feature and the noise combining while applying UM. With the proposed algorithm, it is made possible to enhance the local contrast of an image without amplifying the noise.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.27-40
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• 2022

In this paper, the focus on the removal noise in the binary image based on the variational Bayesian method with the Ising model. The observation and the latent variable are the degraded image and the original image, respectively. The posterior distribution is built using the Markov random field and the Ising model. Estimating the posterior distribution is the same as reconstructing a degraded image. MCMC and variational Bayesian inference are two methods for estimating the posterior distribution. However, for the sake of computing efficiency, we adapt the variational technique. When the image is restored, the iterative method is used to solve the recursive problem. Since there are three model parameters in this paper, restoration is implemented using the VECM algorithm to find appropriate parameters in the current state. Finally, the restoration results are shown which have maximum peak signal-to-noise ratio (PSNR) and evidence lower bound (ELBO).

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.733-741
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• 2012

The successful launch of Arirang satellites allow the acquisition of high resolution satellite imagery of Korean territory and enables the transition from the conventional cultivation area survey method to new image based methods adopted in advanced nations. In this study, we suggested reasonable sizes of the primary sampling unit and the secondary sampling unit for the satellite imagery based sampling design in 8 provinces preselected for this research. The PSU size was determined mainly in consideration of intracorrelation that shows the degree of homogeneity within each cluster and the efficiency of the image process. For the SSU size, we considered the relative standard error and the differences between the land cover maps produced by the Ministry of Environment and the satellite imagery processed by the National Statistical Office.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.619-628
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• 2012

Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.19-26
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• 2007

In the sigma filter, there is a specialized neighbours distribution scheme in which the sigma value is computed from local statistics. It is designed to modify a standard average filter to preserve edges. However this filter is vulnerable to details-enhancement and conventional sigma approaches have been focused on denoising, not enhancing the characteristic area. This paper proposes an adaptive image enhancement algorithm using local statistics and functional synthesis which are utilized for adaptive realization of the enhancement, so that not only image noise may be smoothed but also details may be enhanced. For the local adaptation, parameters are estimated and weighted at each moving window that satisfy the criteria. The experimental results illuminates the effectiveness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.124-133
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• 1999

In this paper, an event detection algorithm is proposed based on the statistics of subblock images. For an event of small size, we first divide each image into smaller subblocks and then for camera trembling, we use the statistics of three kinds of images such as the input image, reference image, and their difference image as features of the event. Simulation results show that the proposed algorithm is much more effective in event detection than the conventional cases based on only the difference image.

• Journal of The Korean Astronomical Society
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• v.29 no.2
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• pp.119-136
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• 1996

To examine the effect of neighboring galaxies on the gravitational lensing statistics, we performed numerical simulations of lensing by many galaxies. The models consist of a galaxy in the rich cluster like Coma, or a galaxy surrounded by field galaxies in $\Omega_0 = 1$ universe with $\Omega_{gal} = 0.1,\;\Omega_{gal} = 0.3\;or\;\Omega_{gal}=1.0\;,\;where\;\Omega_{gal}$ is the total mass in galaxies. Field galaxies either have the same mass or follow Schechter luminosity function and luminosity-velocity relation. Each lensing galaxy is assumed to be singular isothermal sphere (SIS) with finite cutoff radius. In most simulations, the lensing is mainly due to the single galaxy. But in $\Omega_{gal} = 3$ universe, one out of five simulations have 'collective lensing' event in which more than two galaxies collectively produce multiple images. These cases cannot be incorporated into the simple 'standard' lensing statistics calculations. In cases where 'collective lensing' does not occur, distribution of image separation changes from delta function to bimodal distribution due to shear induced by the surrounding galaxies. The amount of spread in the distribution is from a few $\%\;up\;to\;50\%$ of the mean image separation in case when the galaxy is in the Coma-like cluster or when the galaxy is in the field with $\Omega_{gal} = 0.1\;or\;\Omega_{gal}=0.3.$ The mean of the image separation changes less than $5\%$ compared with a single lens case. Cross section for multiple image lensing turns out to be relatively insensitive to the presence of the neighboring galaxies, changing less than $5\%$ for Coma-like cluster and $\Omega_{gal}=0.1,\;0.3$ universe cases. So we conclude that Coma-like cluster or field galaxies whose total mass density $\Omega_{gal}<0.3$ do not significantly affect the probability of multiple image lensing if we exclude the 'collective lensing' cases. However, the distribution of the image separations can be significantly affected especially if the 'collective lensing' cases are included. Therefore, the effects of surrounding galaxies may not be negligible when statistics of lensing is used to deduce the cosmological informations.

• Spatial Information Research
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• v.18 no.3
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• pp.73-83
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• 2010

This study presents a method to segment an image, employing the statistics observed at each pixel location across sequential frame images. In the acquisition and analysis of spatial information, utilization of digital image processing technique has very important implications. Various image segmentation techniques have been presented to distinguish the area of digital images. In this study, based on the analysis of the spectroscopic characteristics of sequential frame images that had been previously researched, an image segmentation method was proposed by using the randomness occurring among a sequence of frame images for a same scene. First of all, we computed the mean and standard deviation values at each pixel and found reliable pixels to determine seed points using their standard deviation value. For segmenting an image into individual regions, we conducted region growing based on a T-test between reference and candidate sample sets. A comparative analysis was conducted to assure the performance of the proposed method with reference to a previous method. From a set of experimental results, it is confirmed that the proposed method using a sequence of frame images segments a scene better than a method using a single frame image.