• 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).

• Journal of Distribution Science
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• v.22 no.7
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• pp.33-42
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• 2024

Purpose: The furniture industry has a huge potential for growth in Indonesia. Due to Indonesia's vast natural resources, furniture designers, makers, and retailers are given ease of access. The research analyzes the influence of service quality, promotion, product, and price on brand image and purchase intention in Indonesia's furniture distribution channels. Research design, data, and methodology: The variables used are service quality, promotion, product, price, brand image, and purchase intention. This research is cross-sectional research, which will be conducted among the furniture consumers in Indonesia, from the Instagram followers of a community as of 31 July 2023 with 837.5 thousand followers. The tools that will be used are surveys, conducted according to the sample size and processed using SMARTPLS 4 and the SEM-PLS model. Results: The findings urge that some variables have a significant influence on purchase intention directly but become less significant when influenced by brand image. Some variables can influence purchase intentions significantly through brand image, even if the certain variable did not have a significant influence on purchase intention directly. Conclusions: By knowing the significance of the variables towards brand image and purchase intention, ones with major influence can be implemented as a strategy to improve marketing in Indonesian furniture distributors.

• Journal of Distribution Science
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• v.22 no.7
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• pp.63-71
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• 2024

Purpose: The main objective of this research is to encourage the increase of local MSME businesses through the sustainable tourism sector in South Sulawesi by proposing a research model that focuses on increasing revisit intention. This, in turn, is expected to stimulate local trade and strengthen tourism attractiveness. Research design, data and methodology: A quantitative research method involving 190 domestic tourist respondents was employed, utilizing a questionnaire for data collection. Structural Equation Modeling (SEM) analysis through AMOS software was applied, and the Sobel test to assess indirect effects. Results: The research findings indicate that memorable customer experiences and travel motivations significantly influence destination image. However, travel motivation does not significantly affect revisit intention. Furthermore, memorable customer experiences and destination image significantly impact revisit intention. Notably, destination image plays a significant mediating role in the relationship between travel motivation and increased revisit intention. Conclusions: Memorable customer experiences and travel motivations directly contribute to the formation of a more positive destination image. Furthermore, memorable customer experiences drive the revisit intention, but travel motivation is not significant. Memorable customer experiences only influence revisit intention through the formed destination image. Additionally, the improvement of memorable customer experience and destination image increased revisit intention.

• Journal of Distribution Science
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• v.22 no.4
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• pp.79-89
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• 2024

Purpose: This study aims to enhance airport service quality by examining their impact on brand image, advocacy, and mediating brand trust in the aviation service distribution sector. Research Design, Data, and Methodology: Using existing literature, we propose a structural model exploring the relationships between key components which are service quality, brand trust, brand Image and brand advocacy. An online survey, based on prior literature, was administered to 287 Koreans who have experienced using facilities or services at Incheon International Airport (IIA). Statistical analysis employed confirmatory factor analysis (CFA) and structural equation modelling (SEM). Results: Research findings show significant impacts of airport service quality on brand trust. Increased brand trust positively influences airport brand image and advocacy. Conclusion: The study emphasizes the aviation industry's potential to boost brand trust through improved airport service quality via users' interactions. Service quality is critical factors in building brand trust. The findings emphasize the critical role of service quality in fostering brand trust. It underscores the importance of user's satisfaction with service quality in fostering brand trust which can lead to brand image and brand advocacy. The aviation industry should formulate policies and strategies to enhance brand trust improved service quality, thereby improving brand image and brand advocacy.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.213-217
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• 1998

This study overviews existing evaluation methods of classification accuracy using confusion matrix proposed by Cohen in 1960's, and proposes ISDd(Index of Spatial Distribution by distance) and ISDs(Index of Spatial Distribution by scatteredness) for the evaluation of spatial distribution of satellite image classification errors, which has not been tried yet. Index of spatial distribution offers the basis of decision on adoption/rejection of classification results at sub-image level by evaluation of distribution, such as status of local aggregation of misclassified pixels. So, users can understand the spatial distribution of misclassified pixels and, can have the basis of judgement of suitability and reliability of classification results.

• International Commerce and Information Review
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• v.8 no.4
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• pp.3-22
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• 2006

Recently, the use of digital contents and demand have been increased with expanding users of internet. Thus, the importance of digital contents distribution site's has been increased that deal in commercially. The model that measuring acceptance of web sites is studying lively, however, the web sites that dealing and distributing specific goods to be called digital contents have insufficient theoretical base and model about acceptance of customers. Also, the research that acceptance of existing commercial web sites have limitation to explain systematically which influence on acceptance of digital contents distribution sites. Because, those research connect directly the feature of web sites, the purchase of web sites or the feature of buyers and acceptance. For that reason, it's hard to reflect the feature of digital contents. In this research, to measure customers' acceptance of web sites that distribute digital image, it is based on Technology Acceptance Model by Davis. This research find out the significant cause from survey by users of digital image distribution site. and TAM which has been adapted the analyzation of new site's acceptance can explain the state of digital image distribution site use. This research let us know the evaluation of digital image distribution site and operating strategy as a new business model.

• Journal of Distribution Science
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• v.21 no.10
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• pp.51-61
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• 2023

Purpose: Research focuses on private universities' professional education in a competitive educational environment. Due to increased competition in the higher education industry, private universities are under pressure to improve their marketing strategies and better understand their prospective students. This study intends to investigate how information sources are used and modified by Indonesian university students when making decisions. Research design, data and methodology: This research is a case study in Riau province, which includes active university students registered in the government database. Data was collected using a questionnaire distributed via Google Forms to students at a private university, and 164 students completed the questionnaire. Results: The results show that the influence of technical quality, functional quality, and image cannot affect word of mouth, and technical quality cannot affect consumer attitudes. The results show that the distribution of high service quality and high image will encourage people to share their experiences by word of mouth to build evaluation attachment in college selection. and found that a good campus image has no direct impact on word of mouth. The spread of an excellent campus image only attracts students to evaluate it. The more talk about the distribution of service quality, the higher the decision to choose the service.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.122-128
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• 1998

This paper presents compression of image sequences based on the classification of interframe difference image blocks. classification process consists of image activity classification and energy distribution classification. In the activity classification, interframe difference image blocks are classified into activity blocks and non-activity blocks using the edge detection. In the distribution classification, activity blocks are further classified into vertical blocks, horizontal blocks, and small activity blocks using the AC energy distribution features. The RBFN, trained with numerical classification results, successfully classifies difference image blocks according to image details. Image sequence compressing based on the classification of interframe difference image blocks using the RBFN shows better compression results and less training time than the classical sorting method and the MLP network.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.525-528
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• 2009

Recently, image classification has been an important task in various fields. Generally, the performance of image classification is not good without the adjustment of image features. Therefore, it is desired that the way of automatic feature extraction. In this paper, we propose an image classification method which adjusts image features automatically. We assume that texture features are useful in image classification tasks because natural images are composed of several types of texture. Thus, the classification accuracy rate is improved by using distribution of texture features. We obtain texture features by calculating image features from a current considering pixel and its neighborhood pixels. And we calculate image features from distribution of textures feature. Those image features are adjusted to image classification tasks using Genetic Algorithm. We apply proposed method to classifying images into "head" or "non-head" and "male" or "female".