• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.1
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• pp.27-32
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• 2018

This work provides the analytical methods to represent the updated form of stiffness or flexibility matrices using the measurements of the first few natural frequencies and the corresponding mode shapes. This study derives the mathematical forms on the variance of stiffness or flexibility matrices to minimize the performance index in the satisfaction of the eigen-function including the residual force depending on the measured data. The proposed methods can be utilized in detecting damage and updating the parameter matrices deviated from the analytical parameter matrices. The validity of the proposed methods is investigated in a numerical experiment of truss structure and the numerical results of stiffness-based and flexibility-based methods are compared. The sensitivity to the external noise is also examined for applying to the practical work.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.160-170
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• 2010

This paper proposes a Haar Wavelet Feature Detector (HWFD) based on the Haar wavelet transform and average box filter. By decomposing the original image using the Haar wavelet transform, the proposed detector obtains the variance information of the image, making it possible to extract more distinctive features from the original image. For detection of interest points that represent the regions whose variance is the highest among their neighbor regions, we apply the average box filter to evaluate the local variance information and use the integral image technique for fast computation. Due to utilization of the Haar wavelet transform and the average box filter, the proposed detector is robust to illumination change, scale change, and rotation of the image. Experimental results show that even though the proposed method detects fewer interest points, it achieves higher repeatability, higher efficiency and higher matching accuracy compared with the DoG detector and Harris corner detector.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.431-438
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• 2020

In this paper, the method to extract lip contour by multiple threshold is proposed. Spyridonos et. el. proposed a method to extract lip contour. First step is get Q image from transform of RGB into YIQ. Second step is to find lip corner points by change point detection and split Q image into upper and lower part by corner points. The candidate lip contour can be obtained by apply threshold to Q image. From the candidate contour, feature variance is calculated and the contour with maximum variance is adopted as final contour. The feature variance 'D' is based on the absolute difference near the contour points. The conventional method has 3 problems. The first one is related to lip corner point. Calculation of variance depends on much skin pixels and therefore the accuracy decreases and have effect on the split for Q image. Second, there is no analysis for color systems except YIQ. YIQ is a good however, other color systems such as HVS, CIELUV, YCrCb would be considered. Final problem is related to selection of optimal contour. In selection process, they used maximum of average feature variance for the pixels near the contour points. The maximum of variance causes reduction of extracted contour compared to ground contours. To solve the first problem, the proposed method excludes some of skin pixels and got 30% performance increase. For the second problem, HSV, CIELUV, YCrCb coordinate systems are tested and found there is no relation between the conventional method and dependency to color systems. For the final problem, maximum of total sum for the feature variance is adopted rather than the maximum of average feature variance and got 46% performance increase. By combine all the solutions, the proposed method gives 2 times in accuracy and stability than conventional method.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1519-1522
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• 2002

In this paper, we propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error that is the difference between an ideally modeled dissolve curve without any correlation and an actual variance curve with a correlation. The dissolve modeling error is determined based on a correlation between two scenes and variances for each scene. First, Candidate regions are extracted by using the characteristics of a parabola that is downward convex, then the candidate region will be verified based on a dissolve modeling error. If a dissolve modeling error on a candidate region is less than a threshold that is defined by a dissolve modeling error with a target correlation, the candidate region should be a dissolve region with a correlation less than the target correlation. The threshold is adaptively determined based on the variances between the candidate regions and the target correlation. By considering the correlation between neighbor scenes, the proposed method is able to be a semantic scene-change detector. The proposed algorithm was tested on various types of data and its performance proved to be more accurate and reliable when compared with other commonly used methods

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.507-520
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• 2001

In this paper, a hybrid algorithm for well detecting both abrupt and gradual scene changes is proposed. This algorithm examines only the candidate intervals for speedup using the binary tree method and skips the intervals that are not candidate. For accuracy, the temporal difference of variance is used to detect the gradual scene changes while the temporal difference of histogram is used to detect the abrupt scene changes. Experimental results show that the proposed hybrid algorithm using the binary tree method works up about 10 times faster that the sequential method and is effective in detecting abrupt scene change and gradual transitions including dissolving and fading.

• Korean Journal of Adult Nursing
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• v.15 no.1
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• pp.5-13
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• 2003

Purpose: The purpose of this study was to compare the effect of breast self-examination (BSE) education between at education and three months. Method: The study subjects were consisted of 30 women chosen from those in a Catholic church in Seoul. The data was collected by using questionnare at two different times: immediately after the BSE education and 3 momths after. Result: At three months, women who performed BSE was 50.0% and the number of BSE practce was 2.53. There was statistically significant change on the score of the knowledge, barrier and practice between at education and three months later. Susceptibility was increased after three month, but wasn't significant different. Confidence, motivation after three months were decreased from the time of initial BSE education and wasn't significantly changed. It was found that motivation about BSE explained 44.2% of variance. Conclusion: The findings showed that the knowledge of BSE, and attitudes and practice were change between at the time of the first survey and at three months. Therefore, the intensive education about BSE can be effective to enhance women's health belief and practice to perform BSE for early detection of breast cancer.

• Journal of Korean Academy of Nursing
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• v.35 no.4
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• pp.721-727
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• 2005

Purpose. This cross-sectional survey was conducted to described the compliance of Breast Self-Examination of middle-aged women using a convenient sample, and to examine relationships between the compliance of BSE and Health Beliefs, and the influencing factors on the compliance of BSE. Methods. The subjects were 373 literate volunteers who were from 41 to 60 years of age who visited 6 public health centers. From June 7, 2004 to August 20, 2004, data were collected by 5 research assistants using a self-report questionnaire. The questionnaire was used to obtain information on the general characteristics, knowledge, health beliefs, and compliance of BSE. Results. The findings of this study suggested that there were significant differences in the scores of the perceived susceptibility and severity between compliers and non-compliers of the BSE. BSE compliance was significantly correlated with knowledge, perceived susceptibility, and perceived severity. The most powerful predictor of BSE compliance was the perceived susceptibility. The perceived susceptibility, the perceived severity, the knowledge and educational level accounted for $41.8\%$ of the variance in middle aged women's BSE compliance. Conclusion. Increase in knowledge about breast cancer, with a concomitant increase in both perceived susceptibility and perceived severity could produce a subtle cue or motivating force sufficient to affect a behavior change. Further research is needed to examine the qualitative difference between BSE and other early detection behaviors.