• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.993-1001
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• 2000

This research features a method that quantitatively evaluates the performance of edge detection algorithms. Contrary to conventional methods that evaluate the performance of edge detection as a function of the amount of noise added to he input image, the proposed method is capable of assessing the performance of edge detection algorithms based on chosen parameters that influence the performance of edge detection. We have proposed a quantitative measure, called average performance index, that compares the average performance of different edge detection algorithms. We have applied the method to the commonly used edge detectors, Sobel, LOG(Laplacian of Gaussian), and Canny edge detectors for noisy images that contain straight line edges and curved line edges. Two kinds of noises i.e, Gaussian and impulse noises, are used. Experimental results show that our method of quantitatively evaluating the performance of edge detection algorithms can facilitate the selection of the optimal dge detection algorithm for a given task.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.77-85
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• 2014

Usually, the skin pigmentation detection and diagnosis are made by clinicians. In this process it is subjective and non-quantitative. We develop an approach to detect and measure the different pigmentation lesions base on computer vision technology. In the paper we study several usually used skin-detecting color space like HSV, YCbCr and normalized RGB. We compare their performance with illumination influence for detecting the pigmentation lesions better. Base on a relatively stable color space, we propose an approach which is RGB channels vector difference characteristic for the detection. After the object region detection, we also use the difference to measure the difference between the lesion and the surrounding normal skin. From the experiment results, our approach can effectively detect the pigmentation lesion, and perform robustness with different illumination.

• Journal of Information Science Theory and Practice
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• v.1 no.2
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• pp.36-57
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• 2013

Blogs are readily available sources of opinions and sentiments which allows bloggers to exert a certain level of influence over the blog readers. Previous studies had attempted to analyze blog features to detect influence within the blogosphere, but had not studied in details influence at the blogger-level. Other studies studied bloggers' personalities with regards to their propensity to blog, but did not relate the personalities of bloggers to influence. Bloggers may differ in their way or manner of exerting influence. For example, bloggers could be active participants or just passive shares, or whether they express ideas in a rational or subjective manner, or they are received positively or negatively by the readers. In this paper, we further analyze the engagement style (frequency, scope, originality, and consistency of the blog postings), persuasion style (appeals to reasons or emotions), and persona (degree of compliance) of individual bloggers. Methods used include similarity analysis to detect the sharing-creating aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. While previous studies analyzed influence at blog site level, our model is shown to provide a fine-grained influence analysis that could further differentiate the bloggers' influence style in a blog site.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.58-63
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• 1995

To access the quality of a fit to a set of data it is always useful to conduct a posteriori analysis involving the examination of residuals, detection of influential data values, etc. Smoothing splines are a type of nonparametric regression estimators for the diagnostic problem. And leverage value, Cook's distance, and DFFITS are used for detecting influential data. Since high leverage points will always have small residuals, the new diagnostic measures including of properties of leverage and residuals are needed. In this paper, we propose FVARATIO version as diagnostic measure in nonparametric regression. Also we consider the rough bound as analogy with linear regression case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.737-743
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• 2008

I compare my parameter estimation methodoloay with existing method, considering both of testing effort and fault detecting rate simultaneously in software reliability modeling. Generally speaking, fault detection/removal mechanism depends on how apply previous fault detection/removal and testing effort of S/W. The fault removal efficiency makes large influence to the reliability growth, testing and removal cost in developing stage S/W. This is very useful measure during all the developing stages and much helpful for the developer to estimate debugging efficiency, and furthermore, to anticipate additional working amount.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.87-96
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• 2013

Influence propagation is an important research issue in social networks. Influence propagation means that the status or the disposition of nodes get changed by new idea, information and gossip propagated by other nodes. Influenced nodes also make other nodes influenced across the network. The influence propagation problem based on 'word of mouth' referral is to find most influential nodes set in networks to maximize influence. In this paper, we study the influence measuring and finding most influential nodes set in Delay-Tolerant Networks. It is difficult to measure exact influential power in Delay-Tolerant networks where network topology is not stable due to the nodal mobility. In this paper, we propose a distributed scheme that each node constructs $k$-clique community structure and estimates local influential power in Delay-Tolerant Networks. Simulation results show that the influential nodes information estimated by proposed scheme is in agreement with a global view of influential nodes information.

• Journal of Community Nutrition
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• v.4 no.3
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• pp.180-186
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• 2002

The food attitudes of 285 Korean female students attending a secondary school in Seoul were examined with respect to the 5 food groups of the Korean Food Tower : grain products, vegetables and fruits, meat, milk, and fats and sweets. An instrument with 22 items was utilized to measure food attitudes toward the five food groups. The items were categorized into five factors through factor analysis to obtain a description of the participants' food attitudes. The five factors are conscious choice of food, health concerns, economics and time influence, interest in foods, and foods that energize. Several facts emerged from examining the food attitudes. The most evident was their response to the items concerning the influence of economics and time on food choice, which the majority consider not limiting their food consumption. Most participants gave favorable responses for vegetables and fruits on all the five factors, but gave unfavorable responses for meat group and fats and sweets in health concerns. They also gave favorable responses for“foods that energize”for all except fats and sweets. Four of the total 25 relationships among food intake (five groups) and food attitudes (five factors) were found to have significant positive correlations (p < .01). (J Community Nutrition 4(3) : 180∼186, 2002)

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.382-384
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• 2004

In this parer, we propose a clustering that perform algorithm using new convergence properties. For detection and optimization of cluster, we use to similarity measure with cumulative probability and to inference the its parameters with MLE. A merits of using the cumulative probability in our method is very effectiveness that robust to noise or unnecessary data for inference the parameters. And we adopt similarity threshold to converge the number of cluster that is enable to past convergence and delete the other influence for this learning algorithm. In the simulation, we show effectiveness of our algorithm for convergence and optimization of cluster in riven data set.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.475-485
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• 2015

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretable compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern of the change.