• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.715-720
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• 2005

This paper presents a hybrid recognition method of first moment of face image and principal component analysis(PCA). First moment is applied to reduce the dimension by shifting to the centroid of image, which is to exclude the needless backgrounds in the face recognitions. PCA is implemented by single layer neural network which has a teaming rule of Foldiak algorithm. It has been used as an alternative method for numerical PCA. PCA is to derive an orthonormal basis which directly leads to dimensionality reduction and possibly to feature extraction of face image. The proposed method has been applied to the problems for recognizing the 48 face images(12 Persons $\ast$ 4 scenes) of 64$\ast$64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.89-97
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• 2015

This study aims to identify parents' recognition of school-enterprise convergence by type of disability. To this end, it analyzes 150 parents with children attending special school in region D, K, and S. As a result, first, parents preferred their children's vocational ability development, employment rate improvement, and manufacturing and supplying method of goods requested by industrial bodies in the community the most and hoped to complete educational courses for the professional education of a job that they wanted to learn and learn the basic ability education for the job. Second, they preferred participation in practical hours and after-school activities and hoped all 2nd and 3rd high school graders to be selected if they wanted, and as compensation for them, wanted appropriate adjustment of class hours or tasks. This suggests that active participation in classes and selection of various types of business seem to be necessary.

• Journal of Korean society of Dental Hygiene
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• v.12 no.5
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• pp.987-997
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• 2012

Objectives : This study was carried out in order to obtain basic data for students' efficient acquirement and instruction of radiography technology in the future by surveying dental hygiene students' recognition of periapical radiography. Methods : This study carried out a questionnaire survey targeting dental hygiene students from December 2009 to December 2010, and obtained the following results. Results : 1. As a result of examining recognition on periapical radiography, the bisecting angle technique was indicated to be averagely $3.84{\pm}0.566$ points. The paralleling technique was indicated to be $2.66{\pm}0.701$ points. 2. As a result of examining about problems given the bisecting angle technique, what had been most difficult given the bisecting angle technique was indicated to be the highest in cone positioning with 34.2%. The most difficulty given deciding on the X-ray vertical-angel irradiation direction was indicated to be the highest with 66.9% in adjusting the cone direction on the virtual bisector. 3. As a result of examining about problems given the paralleling technique, what had been most difficult in the process of the paralleling technique was indicated to be the highest with 56.7% in fixing the film immobilization device inside the mouth. Conclusions : Examining the above results, it is considered that there is a need of understanding morphological and anatomical structure inside the mouth in order to reduce the mistake rate given the periapical radiography, and that it is important to increase skill level by repetitively shooting several times with having enough time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• Korean Journal of Human Ecology
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• v.9 no.2
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• pp.231-243
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• 2000

This study was conducted to investigate the relationship between body image and BMI with satisfaction of own body image, snack intake practices, food intake practices and weight control practices of 226 female college students in Jeonbuk province using questionnaire. The results were summarized as follows : 1. The average height, weight and BMI was 162.08cm, 52.02kg and 19.78, respectively. The average ideal body weight of the subjects was 48.92kg. Ninety percent of the subjects was dissatisfied with their body image. The degree of dissatisfaction was higher in the group who recognized themselves as fat. Most of subjects wanted to lose weight, but as the group having a recognition of thin body image significantly wanted to gain weight (p<0.001). 2. The subjects preferred fruits and juices for snack. The more subjects recognized themselves as fat, the more they restricted snack intake (p<0.05). 3. The rate of skipping meal tends to increase. The subjects did not intake balanced meals and skipped breakfast most (20.7%). The number of food groups taken at breakfast, lunch and dinner was 1.84, 2.25 and 2.55, respectively and the most variable food groups were taken at dinner(p<0.001). Dairy food group intake was low. The duration of meal time was longer in the underweight group by BMI regardless of body image recognition (p<0.05). 4. The weight controlling method was concentrated mostly on decreasing food intake. The more subjects recognized themselves as fat, the more they tried to control weight (p<0.001). Even though 59.5% of the subjects with no weight control experience had no future weight control plans, 50% of the group who recognized themselves as normal or fat did not want to control weight in the future (p<0.01). These results suggest that nutrition education programs and correction programs of food behaviors and weight control should start from focusing on the accurate recognition of body image for college female students.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.808-817
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• 2014

Purpose: This study was to investigate the recognition of skin infection (RSI) and management practice (MP) on caregivers in geriatric hospital. Methods: The subjects were 209 caregivers who work at geriatric hospital in G city and J do. Data were analyzed with ${\chi}^2$-test, t-test. and ANOVA using SPSS 18.0 program. Results: Prevalence rate of caregivers' skin diseases was 76.6%. Diagnosis of Skin disease was contact dermatitis 42.5%, scabies 26.9%, and skin xerosis 25.0%. The Mean RSI score was 3.81 and MP was 4.12. In addition, MP was significantly different by number of bed hospitals (F=4.63, p=.011) and number of caring patients (F=2.67, p=.049). Conclusion: This study will be a useful on continuing education for caregivers and basis for the guidance of medical infection control standards development.

• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.9-16
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• 2006

In this paper, the acoustic model output of CSR(Continuous Speech Recognition) was used to analyze news data News database used in this experiment was consisted of 2,093 articles. Due to the low efficiency of language model, conventional Korean CSR is not appropriate to the analysis of news data. This problem could be handled successfully by introducing post-processing work of recognition result of acoustic model. The acoustic model more robust than language model in Korean environment. The result of post-processing work was made into KIF(Keyword information file). When threshold of acoustic model's output level was 100, 86.9% of whole target morpheme was included in post-processing result. At the same condition, applying length information based normalization, 81.25% of whole target morpheme was recognized. The purpose of normalization was to compensate long-length morpheme. According to experiment result, 75.13% of whole target morpheme was recognized KIF(314MB) had been produced from original news data(5,040MB). The decrease rate of absolute information met was approximately 93.8%.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.369-380
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• 1996

Inductive Loop Detector(ILD) has been commonly used in collecting traffic data such as occupancy time and non-occupancy time. From the data, the traffic volume and type of passing vehicle is calculated. To provide reliable data for traffic control and plan, accuracy is required in type recognition which can be utilized to determine split of traffic signal and to provide forecasting data of queue-length for over-saturation control. In this research, a new recognition model issuggested for recognizing typeof vehicle from thecollected data obtained through ILD systems. Two clustering methods, based on statistical algorithms, and one neural network clustering method were employed to test the reliability and occuracy for the methods. In a series of experiments, it was found that the new model can greatly enhance the reliability and accuracy of type recongition rate, much higher than conventional approa-ches. The model modifies the neural network clustering method and enhances the recongition accuracy by iteratively applying the algorithm until no more unclustered data remains.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.