• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.509-514
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• 2006

Human action recognition is an active research area in computer vision. In this paper, we present a robust method for human action recognition by using combined information of human body shape and motion information with multiple views image sequence. The principal component analysis is used to extract the shape feature of human body and multiple block motion of the human body is used to extract the motion features of human. This combined information with multiple view sequences enhances the recognition of human action. We represent each action using a set of hidden Markov model and we model each action by multiple views. This characterizes the human action recognition from arbitrary view information. Several daily actions of elderly persons are modeled and tested by using this approach and they are correctly classified, which indicate the robustness of our method.

• The Korean Journal of Food And Nutrition
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• v.23 no.3
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• pp.368-375
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• 2010

The purpose of this study was to provide basic informations for educating juveniles on desirable body image recognition by examining their body image recognition and comparing and finding problems according to gender and body mass index (BMI) in middle school students. The average age of the subjects was 15, their average height was 162.5 cm, their average weight was 53.5 kg, and their average BMI was $20.1\;kg/m^2$. Male students wanted to gain weight and female students wanted to lose weight. As for body shape, female students thought that they were fat more often than male students. Also the more overweight the students were, the higher their concerns and experiences toward weight control. Among weight control methods, exercise scored highest in all BMI groups. Over 80% of the respondents answered that their weight control effort were not systematic such as via professional counseling. As for the frequency of eating breakfast and dinner in female students, the more a student was overweight, the lower their frequency of eating breakfast and dinner. Many respondents answered that a typical meal time was '10~20 minutes'. The average eating habit score for all subjects was 38.0. The eating habit scores of male students were significantly higher than those of female students, and there were no significant differences among BMI groups. Eating habit score of 'I have more than two meals with rice everyday' in female overweight group was significantly higher than those of other two groups. These results indicate that the female students' degree of preference for a slim body shape were higher than those of male students. In addition, weight control by students is not systematic through professional counseling; instead they attempt to control their weight by themselves such as a meal skip, which lead to potential danger that they may lose weight carelessly. Therefore, correct body image recognition is greatly needed, and families and schools should make ceaseless efforts to help students properly control their weight and have correct eating attitudes.

• Journal of Korean Home Economics Education Association
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• v.18 no.2 s.40
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• pp.97-110
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• 2006

The purpose of this study was to investigate body image, nutritional knowledge and nutrient intakes of middle school students according to obesity index. In body image awareness, about half of the total subjects recognized their body image correctly. Perceived nutrition knowledge is higher in female students than in male students; however there was no difference about accuracy. All groups had a higher protein intake than recommended levels. In examining the correlation of each factors, there was a significant correlation between satisfaction of body weight and body image recognition(p<0.01).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• Journal of Fashion Business
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• v.11 no.2
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• pp.145-154
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• 2007

The purpose of this study is to analyze University Woman Students' recognition of their physical bodies and the effects that their recognition has on their image and satisfaction of their physical bodies. The result of study is as follows. 1) According to the result of the analysis of the difference between University Woman Students students' practical physical sizes and ideal sizes, it was showed that their heights and burst should be larger than their practical physical sizes but their waist, hip, and weight should be reduced.2) According to the result of the analysis of the factors for the evaluation of University Woman Students students' behavioral body images, Factor I was 'Management of Appearance', factor II was 'Management of Weight' and Factor III was 'Satisfaction of Appearance'. Considering the result of correlation of factors of the body images, it was showed that the more interest they had on their appearances, the more management they had on their weights. 3) They had comparatively positive recognition on their physical bodies. However, regarding their degrees of the satisfactions on their physical parts, they showed somewhat dissatisfaction on all the items of their physical parts. 4) According to the result of the analysis of the correlation between their recognition of the physical types and their satisfaction of them, it was showed that they had more satisfactions when their heights were higher and their weights were lighter. In addition, it was showed that they had more satisfaction when they had larger busts and regarding their waists, hips, upper arms, lower arms, wrists, thighs, and calf measurements, they had more satisfactions when they had thinner ones and regarding their shoulders' measurements, it was showed that they had more satisfactions when they had narrower shoulders' measurements.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.151-160
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• 2002

Cow image processing technique would be useful not only for recognizing an individual but also for establishing the image database and analyzing the shape of cows. A cow (Holstein) has usually the unique speckle pattern. In this study, the individual recognition of cow was carried out using the speckle pattern and the content-based image retrieval technique. Sixty cow images of 16 heads were captured under outdoor illumination, which were complicated images due to shadow, obstacles and walking posture of cow. Sixteen images were selected as the reference image for each cow and 44 query images were used for evaluating the efficiency of individual recognition by matching to each reference image. Run-lengths and positions of runs across speckle area were calculated from 40 horizontal line profiles for ROI (region of interest) in a cow body image after 3 passes of 5$\times$5 median filtering. A similarity measure for recognizing cow individuals was calculated using Euclidean distance of normalized G-frame histogram (GH). normalized speckle run-length (BRL), normalized x and y positions (BRX, BRY) of speckle runs. This study evaluated the efficiency of individual recognition of cow using Recall(Success rate) and AVRR(Average rank of relevant images). Success rate of individual recognition was 100% when GH, BRL, BRX and BRY were used as image query indices. It was concluded that the histogram as global property and the information of speckle runs as local properties were good image features for individual recognition and the developed system of individual recognition was reliable.

• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.3
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• pp.1-16
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• 2017

This study examines the clothing pursuit benefit and pursued image of middle aged men according to lifestyle and body satisfaction. A survey was carried out among male consumers between the ages of 40 and 50, and a total of 322 responses were analyzed by factor analysis, a reliability test, cluster analysis, t-test, and ANOVA. The results of this study were as follows. Middle aged men were classified into four different groups in terms of lifestyles, including health managing, trend-seeking, economic consumption, and proactive lifestyle groups. As a result of looking at the differences in clothing pursuit benefits among four lifestyle groups, two lifestyle groups such as trend-seeking and proactive lifestyle groups both considered individuality more importantly than other clothing pursuit benefits. The proactive lifestyle group considered comfort, social recognition and individuality benefits. As a result of looking at differences in the pursued image of clothing among lifestyle groups, the proactive lifestyle group preferred a classic, refined and mild clothing image. There were significant differences in clothing pursuit benefits and pursued images between high- and low-level groups of body satisfaction. Those with high body satisfaction considered individuality, social recognition, and comfort more than those with low body satisfaction. The high body satisfaction group preferred all clothing images such as mild, classic and refined.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.4
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• pp.444-451
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• 2011

Background: The purpose of this study was to identify weight control behavior according to body mass index and perception of body-image, and factors that affect weight control behavior in female college students. Methods: A convenience sample of 276 female college students agreed to complete a questionnaire. Data were collected from October 5 to October 15, 2009. The data were analyzed using SPSS win 18.0 program with descriptive statistics, Chi-square test and Logistic regression. Results: According to preferred body type, 86.9% of the students who preferred slim, 13.3% who preferred their own body type, and 4.2% who preferred chubby reported using weight control behavior. For weight control behavior according to satisfaction with body type, 82.9% of the students who were dissatisfied with their body type and 52.8% who were satisfied practiced weight control behavior. Variables influencing weight control behavior were body mass index (underweight OR 1.37, p=.050), cognition of the body (chubby/fat OR 2.68, p=.047), and preferred body type (slim OR 1.29, p=.006). Conclusion: The results of this study indicate that, health providers need to educate female college students about appropriate body image and weight control behavior. Also, more studies are needed to identify other factor influencing weight control behaviors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.