• Korean Journal of Community Nutrition
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• v.14 no.6
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• pp.807-816
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• 2009

This day's research analyzed the difference between brand personality, self - image congruity, job satisfaction and their influences towards employees of family restaurants in order to suggest a plan which would induce researcher's interest as well as influencing diversification of management strategies toward dining-out business. The purpose of this research is to analyse the difference between brand personality, self - image congruity, job satisfaction and their influences towards employees of family restaurants. The survey questionnaires were distributed to 300 employees of family restaurants in Seoul from August 1th until August 30th 2009, and 257 of them were used for analysis. The top seven company's were chosen by base on data from 2009 Annual Dinner of the Korea. Statistics handling of this research used SPSS WIN 17.0 statistics package program, which performed frequency analysis, factor analysis, regrssion anlysis. The research result shows, first of all, the relationship between company's brand personality and personal self - image congruity, it shows that the company's brand personality has higher on 'ability/capability, loyalty/fidelity, and strong' the personal self - image congruity appeared higher. The relationship between company's brand personality and social self-image congruity, it shows that the company's brand personality has higher on 'ability/capability and loyalty/fidelity' the social self-image congruity appeared higher. Second of all, in a relation between the self-image congruity and job satisfaction, the personal self-image congruity has shown positive impact on job satisfaction. Third of all, in a relationship between the company's brand personality and job satisfaction, if 'interest or loyalty/fidelity' shows higher on brand personality, than job satisfaction has shown higher.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.595-607
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• 2010

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.991-1001
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• 2015

Digital images acquired by digital devices are used in many fields. Applying statistical methods to the processing of images will increase speed and efficiency. Methods to remove noise and image quality have been researched as a basic operation of image processing. This paper proposes a novel reduction method that considers the direction and magnitude of the edge to remove image noise effectively using statistical methods. The proposed method estimates the brightness of pixels relative to pixels in the same direction based on a robust regression model. An estimate of pixel brightness is obtained by weighting the magnitude of the edge that improves the performance of the average filter. As a result of the simulation study, the proposed method retains pixels that are well-characterized and confirms that noise reduction performance is improved over conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.43-52
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• 2014

In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.191-204
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• 2021

Visual Question Answering (VQA) and image captioning are tasks that require understanding of the features of images and linguistic features of text. Therefore, co-attention may be the key to both tasks, which can connect image and text. In this paper, we propose a model to achieve high performance for VQA by image caption generated using a pretrained standard transformer model based on MSCOCO dataset. Captions unrelated to the question can rather interfere with answering, so some captions similar to the question were selected to use based on a similarity to the question. In addition, stopwords in the caption could not affect or interfere with answering, so the experiment was conducted after removing stopwords. Experiments were conducted on VQA-v2 data to compare the proposed model with the deep modular co-attention network (MCAN) model, which showed good performance by using co-attention between images and text. As a result, the proposed model outperformed the MCAN model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.1
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• pp.112-119
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• 1990

Two adaptation procedures are proposed for image restoration, in which the window shapes of OS filters are changed according to the order statistics of signal. In the first procedure, the 2-dimensional window is the union of the 1-dimensional subwindows whose sizes are fixed. In the second procedure, the 2-dimensional window is the union of the 1-dimensional subwindows whose sizes are variable. Compared with existing procedures, our adaptation procedures using order statistics are computationally efficient. Simulation results show that the filters with adaptive window shapes have good performance for the preservation of edges and details of image, and the noise suppression.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.863-867
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• 1987

When digital image signals are transmitted or stored, they may be usually degraded by impulsive noise such as BSC noise. Though median filtering is a very effective method to reduce the impulsive noise, it brings non-negligible distortion after filtering. Several algorithms have been proposed to reduce such a distortion, but their reconstructed image quality are inadequate in some cases and they have a difficulty in real-time processing. In this paper, an effective filtering algorithm which can not only reduce the noise effectively but also preserve the edges well and lessen the distortion greatly, is presented. The proposed algorithm is an adaptive algorithm of median filter using local statistics, based on the characteristics of human eyes. The adaptive algorithm results shwo performance improvement of up to 3-4 dB over the nonadaptive one.

• International Journal of High-Rise Buildings
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• v.2 no.2
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• pp.105-113
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• 2013

Recent studies proved turbulent flow properties in high-rise building models differ from those in low-rise building models by comparing turbulent statistics. Although it is important to understand the flow characteristics within and above high-rise building models in the study of urban environment, it is still unknown and under investigation. For this reason, we performed wind tunnel experiment using Particle Image Velocimetry (PIV) to investigate and identify the turbulent flow properties and characteristic flow patterns in high-rise building models. In particular, we focus on instantaneous flow field near the canopy and extracted flow field when homogeneous flow field were observed. As a result, six characteristic flow patterns were identified and the relationship between these flow patterns and turbulent organized structure were shown.

• Journal of the Korean Society of Costume
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• v.64 no.3
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• pp.62-76
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• 2014

The purpose of this study is to classify the facial images and analyze shape characteristics of Korean women in their 20s. Previous research and survey were used for the study, the surveys targeted 220 university students in their 20s. The subjects of the experiment were 20-24 year-old Korean women. SPSS 12.0 statistics program was used to analyze the results, and factor analysis, Cronbach's ${\alpha}$ reliability analysis, and multidimensional scaling(MDS) were executed. The results of the study are as follows: First, the facial image types of Korean women in their 20s were classified into 4 categories as 'Youthfulness', 'Classiness', 'Friendliness', and 'Activeness'. Second, the multi-dimensional scaling method was performed and two orthogonal dimensions for the facial image of the Korean women were suggested: strong - soft and classy-friendly. Third, by analyzing the basic statistics concerning the structural characteristics of facial image of Korean women, there were differences in structural characteristics that form the facial images. Especially, significant difference appeared in items related forehead, eyebrows, eyes and jaw.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.277-286
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• 2009

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we introduce a robust brain registration technique for correcting the difference between two temporal images by the different coordinate systems in MR and CT image obtained from the same patient. Two images are registered where this measure is minimized using a modified conditional entropy(MCE: Modified Conditional Entropy) computed from the joint histograms for the intensities of two given images, we conduct the rendering for visualization of 3D volume image.