• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Speech Sciences
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• v.15 no.1
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• pp.37-51
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• 2008

The main goal of this study is to examine if there is a difference in the utilization of a vowel length cue between Korean and Japanese L2 learners of English in their perception and production of postvocalic coda contrast in English. Given that Japanese subjects' performances on the identification and production tasks were much better than Korean subjects' performance, we may support the prediction based on the Feature Hypothesis which maintains that L1 phonological features can facilitate the perception of L2 acoustic cue. Since vowel length contrast is a phonological feature in Japanese but not in Korean, the tasks, which assess L2 leaners' ability to discriminate vowel length contrast in English, are much easier for the Japanese group than for the Korean group. Although the Japanese subjects demonstrated a better performance than the Korean subjects, the performance of the Japanese group was worse than that of the English control group. This finding implies that L2 learners, even Japanese learners, should be taught that the durational difference of the preceding vowels is the most important cue to differentiate postvocalic contrastive codas in English.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.10
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• pp.76-88
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• 1997

Based on the principles described in Part I, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1648-1672
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• 2020

There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image's inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• Korean Journal of Social Welfare Studies
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• no.39
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• pp.287-314
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• 2008

This paper tests the stigma hypothesis and administration hypothesis on the illegal take-up group and non take-up group in the National Livelihood Security Program. A set of survey data, using multinomial logistic model, was analyzed for this purpose. Compared with the legal take-up group, the feature of illegal take-up group which has more workable household supports the administration hypothesis - the low skill of means-test office would increase the possibility of benefit fraud. The features of non take-up group support both the stigma hypothesis - the stigma prevents eligible person from participating in the social assistance program, and the administration hypothesis - the administration office is apt to make error to deny the eligibility of person who has supposed family supporters.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Journal of Distribution Science
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• v.19 no.1
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• pp.75-87
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• 2021

Purpose: The objective of this research is to identify the core aspects of persuasive factors influencing consumer's experiential value towards using Augmented Reality (AR) try-on feature while shopping cosmetic products online. The conceptual framework of this study is adopted and integrated from the theoretical study on how narrative experience, media richness, and presence affect the formation of experiential value in the augmented reality interactive technology (ARIT) process. Research design, data and methodology: The sample (n = 550) were collected from online and offline questionnaires by using stratified random sampling and purposive sampling methods. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used to analyze the data to confirm goodness-of-fit of the model and hypothesis testing. Results: The results indicated that media richness induced higher experiential value (consumer ROI, playfulness, service excellence and aesthetics), followed by narrative experience and presence towards using AR try-on feature. Conclusions: Consumer's experiential value towards using AR try-on feature when shopping cosmetic products online rely on media richness, narrative experience and presence respectively. Therefore, marketing practitioners are recommended to develop the feature design and content to be more useful, authentic, user-friendly and entertaining to better connect and provide confidence to consumers when shopping cosmetics online.

• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.4
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• pp.1-15
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• 2021

Purpose - In a difficult time for a firm, it seems impossible to change circumstances by a firm. Nevertheless, the firm must do whatever it can do by however it can do. Therefore, the purpose of this study is to analyze the effect of HRM practice on organizational effectiveness with the status quo of the firm as a moderator. Based on the result of this study, the managerial implication could be suggested as a contextual response to each status quo of the firm in improving and managing organizational effectiveness by HRM practice. Research design, data, and methodology - This study measured organizational effectiveness with employee satisfaction and organizational commitment. HRM practice includes two HR management areas, HR system, and HR attitude. HR system includes education & training and additional wage welfare. HR attitude includes employee stress and empowerment. As for the status quo of the firm, this study considered three construct; firm feature, strategic feature, environment change feature. This study analyzed 397 employees of 24 company data from the 7th HCCP of KRIVET. Result - Hypothesis 1 through Hypothesis 3 were partially supported. The results of this study suggest that to increase organizational effectiveness(job satisfaction and organizational commitment), employee stress and education & training participation need to be managed. And circumstance of an organization as given the Status Quo of the firm needs to be managed differently like firm size, environment change in demand, and technology. Conclusion - This study suggests best-practice implications based on the result between HRM practice and organizational effectiveness. And also suggest differentiation in management to increase the best-fit in management.