• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.46-53
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• 2011

This paper proposes a SURF (Speeded Up Robust Features) based face recognition method which is one of typical interest point extraction algorithms. In general, SURF based object recognition is performed in interest point extraction and matching. In this paper, although, proposed method is employed not only in interest point extraction and matching, but also in face image rotation and interest point verification. image rotation is performed to increase the number of interest points and interest point verification is performed to find interest points which were matched correctly. Although proposed SURF based face recognition method requires more computation time than PCA based one, it shows better recognition rate than PCA algorithm. Through this experimental result, I confirmed that interest point extraction algorithm also can be adopted in face recognition.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.2
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• pp.299-307
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• 1995

The extraction of distinct points such as corner points and circular features is a basic procedure in digital photogrammetry and computer vision. This paper describes the extraction of image features from the raw images (gray value images), especially Forstner interest corner points. The mathematical model of the Forstner interest operator as well as the behavior in the presence of noise are investigated. Experiments with real images prove the feasibility of the Forstner interest operator in the field of Digital Photogrammetry.

• Journal of Korean Academy of Nursing
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• v.3 no.1
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• pp.53-66
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• 1972

There is an urgent need to improve the tool predicting success or failure of academic achievement of nursing in Korea so as to identify as early as possible those students who should receive special instruction and to improve screening procedures for admission of nursing. The main purpose of this study is to identify the correlation between the grade point averages of courses learned and their psychological test characteristics in a baccalaureate nursing program. All 240 students, except freshmen, enrolled in Nursing Department of Seoul National University in the spring semester, 1972, participated in this study. All of the subjects completed the psychometric tests such as interest test, personality test and test of self-concept. Total grade point averages, grade point averages of general education subjects, of supporting science courses and of professional education subjects were used as performance criteria of the students. Through the calculation of product-moment correlation coefficients between the test scores and four grade point averages of each class and of total subjects, the following findings and recommendations were obtained. 1. There was so much variation in characteristics of interest test correlated with academic achievement of nursing students in each class. 2. Since the school objectives, curriculum and teaching strategies may affect predictive efficiency of characteristics of students'interest test, interest test must-be utilized in a homogeneous group in order to predict school achievement. 3. Characteristics of interest test positively correlated at significant level with total grade point averages of all subjects were scientific interest-biological, scientific interest-physical, and humanitarian interest. Scientific interest-physics: was the only characteristic positively correlated at significant level with total grade point averages and grade point averages of professional courses. 4. There were various patterns in characteristics of personality test correlated with school achievement of nursing students by class pattern and personality maturation as they progress toward higher classes. 5. A characteristic of personality test, responsibility, is in high positive correlation with academic achievement in the upper division of classes. 6. Responsibility was the sole personality factor positively correlated at significant level with total grade point averages and grade point averages of nursing courses in the total number of students. 7. There were very different correlation coefficients between characteristics of self-concept test and academic achievement according to the type of each class and type of courses they learned. 8. Characteristics of self-concept test positively correlated at significant level with total grade point averages and grade point averages of nursing courses of all students were physical self and row variability. Those who have positive concept on their own physical status and who are deficient in self-concept were higher in total grade point averages and grade point averages of professional courses than other students. 9. Scores of professional courses offered in freshmen and sophomore classes were in positive correlation with limited number of characteristics of psychological tests. In pursuit of a tool predicting successful academic achievement of nursing students, their G.P.A. during the junior and senior year of nursing will serve as the more reasonable criteria. 10. Junior students of this school were in higher positive correlation with many psychological factors than other classes.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.49-55
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• 2015

In this paper, we propose a fast interest point detection method using SURF algorithm. Since the SURF algorithm needs a great computations to detect the interest points and obtain the corresponding descriptors, it is not suitable for real-time based applications. In order to overcome this problem, the interest point detection step is parallelized by OpenMP and SIMD based on analysis of the scale space representation process and localization one in the step. The simulation results demonstrate that processing speed is enhanced about 55% by applying the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• Women's Health Nursing
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• v.8 no.4
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• pp.608-618
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• 2002

The purpose of this study is to analyze the type of ego states and stress coping style on female college students who are in the course of nursing study. This study is performed in the view of Transactional Analysis and designed to scrutinize descriptive correlations between the type of ego states and stress coping style. The subject is consists of 144 freshmen and sophomore, 138 junior and senior students group, who are students of K nursing college located in Seoul. The sampling investigation period is on Sept. 14, 2002 to Oct. 26, 2002. The measuring instrument used for Transactional Analysis ego state is 50 items Ego-gram research paper devised by Dusay(1997). For studying coping style, Folkman & Lazarus's measurement(1984) was adopted, which is translated and modified by Han, and Oh,(1990). Statistic average and standard deviation were generated by using SPSS PC+, t-test and Pearson correlation. The results were as follows: 1) In the type of ego states on both groups(lower group : freshmen, sophomore upper group : junior, senior) indicated the arithmetic apex NP(maximum value), then the point A was high and the data made a down slope to point AC. In the comparison to type of ego states between two groups, only at point CP, the data value of upper year students represented higher than that of lower year ones by C(t=2.28, p=.023). In the psychological energy level of ego states, both groups indicated average level.2) Stress coping style of whole students were highly and affirmatively dedicated to research. Consecutive consequences follow like this(high to low) : the central point of problem, search for social support, hopeful aspect and indifference. Especially hopeful aspect(t=.67, p=.05), relaxation of tension(t=-2.16, p=.03) made significant difference each other in the view of arithmetic calculation 3) While verifying coping style in terms of ego states level between lower and upper students group, In type CP, high level ego states group indicated significant difference on stress coping style area than low leveled group and made such sequences as the central point of problem, hopeful aspect, search for social support, positive interest and relaxation of tension. In type NP, sequences such as the central point of problem, search for social support, positive interest and relaxation of tension were emerged with little differences. In type A, the central point of problem, positive interest and relaxation of tension. In type FC, hopeful aspect, search for social support, positive interest and relaxation of tension. In type AC, hopeful aspect and indifference were derived significantly different(p<.05). 4) In the aspect of relation between ego states and coping style, type CP presented the central point of problem and relaxation of tension, type NP presented positive interest, search for social support and the central point of problem, type A showed the central point of problem, positive interest and relaxation of tension, type FC showed relaxation of tension, positive interest, search for social support, indifference and the central point of problem, type AC showed hopeful aspect, indifference and the central point of problem. All the sequence shown above had high-to-low procedure and represented static relations each other(p<.05).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.43-50
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• 2015

Google, Yahoo, Daum and Naver has the POI(point of interest) service. And POI on the map is expending to social commerce, SNS, social game and social shopping. At the same time the uses's position on the map is the starting point of the Humanities Story. That means our current position is the place for stories of tales, children's song, fictional characters, the film background, lyrics and the birth of great people. This study points out that service has the limited to cafe, restaurant and hospital, and suggests the Humanities fusion Map Service which is combined with the POI information.

• ETRI Journal
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• v.37 no.4
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• pp.766-771
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• 2015

Robust motion estimation for human-computer interactions played an important role in a novel method of interaction with electronic devices. Existing pose estimation using a monocular camera employs either ego-motion or exo-motion, both of which are not sufficiently accurate for estimating fine motion due to the motion ambiguity of rotation and translation. This paper presents a hybrid vision-based pose estimation method for fine-motion estimation that is specifically capable of extracting human body motion accurately. The method uses an ego-camera attached to a point of interest and exo-cameras located in the immediate surroundings of the point of interest. The exo-cameras can easily track the exact position of the point of interest by triangulation. Once the position is given, the ego-camera can accurately obtain the point of interest's orientation. In this way, any ambiguity between rotation and translation is eliminated and the exact motion of a target point (that is, ego-camera) can then be obtained. The proposed method is expected to provide a practical solution for robustly estimating fine motion in a non-contact manner, such as in interactive games that are designed for special purposes (for example, remote rehabilitation care systems).