• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3041-3043
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• 2000

GUI(graphical user interface) has been the dominant platform for HCI(human computer interaction). The GUI-based style of interaction has made computers simpler and easier to use. However GUI will not easily support the range of interaction necessary to meet users' needs that are natural, intuitive, and adaptive. In this paper we study an approach to track a hand in an image sequence and recognize it, in each video frame for replacing the mouse as a pointing device to virtual reality. An algorithm for real time processing is proposed by estimating of the position of the hand and segmentation, considering the orientation of motion and color distribution of hand region.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.504-516
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• 2011

In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.521-528
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• 2007

The overall objective of this research was to investigate various combination of segmentation parameters and to improve classification accuracy of object-oriented classification. This research presents a method for evaluation of segmentation parameters by calculating Moran's I and Intrasegment Variance. This research used Landsat-7/ETM image of $11{\times}14$ Km developed area in Ansung, Korea. Segmented images are generated by 75 combinations of parameter. Selecting 7 combinations of high, middle and low grade expected classification accuracy was based on calculated Moran's I and Intrasegment Variance. Selected segmentation images are classified 4 classes and analyzed classification accuracy according to method of objected-oriented classification. The research result proved that classification accuracy is related to segmentation parameters. The case of high grade of expected classification accuracy showed more than 85% overall accuracy. On the other hand, low ado showed around 50% overall accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1106-1110
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• 2004

A human sign language is generally composed of both static and dynamic gestures. Each gesture is represented by a hand shape, its position, and hand movement (for a dynamic gesture). One of the problems found in automated sign language translation is on segmenting a hand movement that is part of a transitional movement from one hand gesture to another. This transitional gesture conveys no meaning, but serves as a connecting period between two consecutive gestures. Based on the observation that many dynamic gestures as appeared in Thai sign language dictionary are of quasi-periodic nature, a method was developed to differentiate between a (meaningful) dynamic gesture and a transitional movement. However, there are some meaningful dynamic gestures that are of non-periodic nature. Those gestures cannot be distinguished from a transitional movement by using the signal quasi-periodicity. This paper proposes a hybrid method using a combination of the periodicity-based gesture segmentation method with a HMM-based gesture classifier. The HMM classifier is used here to detect dynamic signs of non-periodic nature. Combined with the periodic-based gesture segmentation method, this hybrid scheme can be used to identify segments of a transitional movement. In addition, due to the use of quasi-periodic nature of many dynamic sign gestures, dimensionality of the HMM part of the proposed method is significantly reduced, resulting in computational saving as compared with a standard HMM-based method. Through experiment with real measurement, the proposed method's recognition performance is reported.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.2-105
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• 2001

This paper studies continuous Korean Sign Language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is a very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt Hidden Markov Model (HMM). Using these methods, we recognize 5 KSL sentences and obtain 94% recognition ratio.

• Journal of Fashion Business
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• v.27 no.4
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• pp.21-37
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• 2023

This study aimed to classify the hand types of adult males aged 20 to 69 years using three-dimensional measurement data from the 2020 8th Korean Anthropometric Survey, the latest measurement data from the National Institute of Standards and Technology Size Korea, and explore the characteristics of each type. Through this, I aimed to draw implications for the development of work gloves. The factors that make up an adult male's hand were categorized into hand and finger thickness factors, palm length factors, and finger length factors. Adult male hands were categorized into four types: small, thin hands and long fingers; thick, long fingers; medium, short hands and fingers; and large, thin, short fingers. The analysis showed that the younger the age, the more slender and long the hands and fingers, and as age increased, hands and fingers became shorter and thicker. Implications for the development of work gloves included the need for size segmentation based on the age of the work glove user, changes in the way glove dimensions are set based on hand length and hand circumference, and the need to segment gloves by the type of work. Hand typing in future research should be done according to occupational groups, and glove patterns should be developed for each type of work based on the results of this study.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.10
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• pp.1611-1620
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• 2010

This research compares the difference of each preference segments' subjective hands and sensibilities in order to analyze the correlations among preference, subjective hands, and sensibilities. Preference segments were classified into wool, acrylic, and long stitch length-preferred clusters in previous research. To evaluate the subjective hands and sensibilities of knit fabrics, the 20's and 30's women rated twelve knit fabrics by touching, using a questionnaire with a seven-point semantic differential scale. These twelve knit fabrics were differentiated by controlling the mixture ratio and stitch length using a computer-controlled automatic flat knit machine. The difference of each preference segments' subjective hands and sensibilities was determined using the conjoint analysis. The clusters perceived the subjective hands and sensibilities differently according to preferred constituent characteristics. There was no correlation between surface unevenness and preference in wool-preferred cluster, while there were negative correlations in other clusters. The acrylic-preferred cluster had a preference in coolness compared to other clusters; in addition, the long stitch-preferred cluster preferred flexibility/bulkiness and extensibility than the others. All clusters preferred modem and natural sensibilities that were caused by different constituent characteristics of knit fabrics.

• Fashion & Textile Research Journal
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• v.13 no.6
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• pp.827-837
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• 2011

The purpose of this study was to observe premium handbag users who are 20 to 30 year old of age, to determine purchase behavior and design preference of handbags by lifestyle and age variable to help market segmentation. A survey instrument was used. A sample was selected by quota sampling method from 20 to 30 aged Korean women, and reliable 538 data were analyzed by SPSS. Cluster type towards lifestyle and age were independent variable. There are two types of lifestyle clusters toward luxury handbags; Strongly favored and weakly favored group toward luxury handbags. Strongly favored group of luxury goods considered more brand, country of origin, and trend, while young people considered more new arrival of design in purchase of products. Preferred design type of hand bag was statistically associated with age variable. 20s consumers preferred shopper bag style and big size of handbags. Leather was preferred by the group of the strongly favored luxury goods. Through this research finding, we hope handbag brand market segmentation will be based on lifestyle and age variable to reflect customer's demand.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.1-10
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• 2006

This paper presents a development of a mobile sign language recognition system for daily communication of deaf people, who are sign dependent to access language, with hearing people. The system observes their sign by a cap-mounted camera and accelerometers equipped on wrists. To create a real application working in mobile environment, which is a harder recognition problem than lab environment due to illumination change and real-time requirement, a robust hand segmentation method is introduced and HMMs are adopted with a strong grammar. The result shows 99.07% word accuracy in continuous sign.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.4-10
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• 2000

The work presented in this paper is about a postprocessor, which improves the performance of automatic speech segmentation system by correcting the phoneme boundary errors. We propose a postprocessor that reduces the range of errors in the auto labeled results that are ready to be used directly as synthesis unit. Starting from a baseline automatic segmentation system, our proposed postprocessor trains the features of hand labeled results using multi-layer perceptron(MLP) algorithm. Then, the auto labeled result combined with MLP postprocessor determines the new phoneme boundary. The details are as following. First, we select the feature sets of speech, based on the acoustic phonetic knowledge. And then we have adopted the MLP as pattern classifier because of its excellent nonlinear discrimination capability. Moreover, it is easy for MLP to reflect fully the various types of acoustic features appearing at the phoneme boundaries within a short time. At the last procedure, an appropriate feature set analyzed about each phonetic event is applied to our proposed postprocessor to compensate the phoneme boundary error. For phonetically rich sentences data, we have achieved 19.9 % improvement for the frame accuracy, comparing with the performance of plain automatic labeling system. Also, we could reduce the absolute error rate about 28.6%.