• Journal of architectural history
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• v.7 no.1 s.14
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• pp.81-92
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• 1998

According to Post-modernists including deconstructivists, as Modernism is changed into Post-modernism, the paradigm is shifted from consciousness to language. The paradigm of consciousness corresponds to representational language, and the paradigm of language to self-referential one. In post-modern age most of architects are wandering what kind of language architecture is. Some theorists contend that architecture is representational, and others that it is self-referential. Allan Colquhoun, who is known as one of the best architectural theorists inUnited States, accepts both the former and the latter, but fails to reveal the meaning and the limitation, of the two languages. Although he believes that the representational language of architecture ('figure') is the source of self-referential language of architecture('form'), he never clearly answers what kind of language architecture. In order to overcome the limitation and the meaning of Colquhoun's figure and form, and synthesize the two language, this essay appropriates Martin Heidegger's some concepts, 'ready-to-hand,' 'present-at-hand' and 'being-in-the-world' to make a theoretical framework for 'image' which prevails over and synthesizes 'form' and 'figure.' Since Image is based upon both 'being-in-the world' and 'ready-to-hand,' it is the source of 'form' and 'figure.' When 'image' is fragmented, the former and the latter emerge. Image is therefore both the former and the latter because it represents and self-refers a world as a reality.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.107-118
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• 2007

This paper proposes a 3D avatar which expresses sign language in a very using lips, facial expression, complexion, pupil motion and body motion as well as hand shape, Hand posture and hand motion to overcome the limitation of conventional sign language avatars from a deaf's viewpoint. To describe motion data of hand and other body components structurally and enhance the performance of databases, we introduce the concept of a hyper sign sentence. We show the superiority of the developed system by a usability test through a questionnaire survey.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.333-336
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• 1996

The sign language is a method of communication for deaf person. For sign communication, sign language and manual alphabet are used continuously. In this paper is proposed a system which recognize Korean sign language(KSL) and Korean manual alphabet(KMA) continuously. For recognizing KSL and KMA, basic elements for sign language, namely, the 14 hand directions, 23 hand postures, and 14 hand orientations are used. At first, this system recognize current motion state using speed and change of speed in motion by state automata. Using state, basic element classifiers using Fuzzy Min-Max Neural Network and Fuzzy Rule are executed. Meaning of signed gesture is selected by using basic elements which was recognized.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.85-93
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• 2005

In this paper, we propose recognition system which tracks the unmarked hand of a person performing sign language in complex background. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour and recognize sign language by applying improved centroidal profile method. In the experimental results for 6 kinds of sing language movement, unlike existing methods, we can stably recognize sign language in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and $90\sim100%$ for each movement at 15 frames/second.

• 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 Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.593-598
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• 2014

Hand gesture recognition based on vision is a challenging task in human-robot interaction. The sign language of finger spelling alphabets has been tested as a kind of hand gesture. In this paper, we test hand gesture recognition by detecting the contour shape and orientation of hand with visual image. The method has three stages, the first stage of finding hand component separated from the background image, the second stage of extracting the contour feature over the hand component and the last stage of comparing the feature with the reference features in the database. Here, finger spelling alphabets are used to verify the performance of our system and our method shows good performance to discriminate finger alphabets.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1067-1075
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• 2006

This paper proposes a advanced representation method of hand motion by cheremes analysis in korean sign language. The proposed method is the representation method which apply to the hand motion used in KSL(Korean Sign Language) to represent rich and united hand motion. Words or sentences in KSL are completed by combination of elements called as Cheremes, that is, a hand movement orientation, a finger shape, a hand position, etc. In this paper, Cheremes composing the KSL is divided and represented by 5 elements: the hand movement orientation(HMO), finger shape(FS), hand orientation(HO), hand position(HP) and number of using hand (HN). Each cheremes is expressed by more various characteristics. For example, The hand movement orientation means orientations which the hand move while the sign language is done and can be expressed by 17orientation components. The finger shape means various shapes which fingers can take and represented by 17 components. The Orientation of hand is expressed by 2 characteristics according to whether we use the palm of the hand or the back. The position of hand means specific regions in body which hand(s) is placed while the sign language is done and divided by 8 regions. Finally, the number of hand means whether use only one hand or both hands and is expressed by 2 characteristics. The proposed method has been tested with KSL words and sentences and the results have shown that they can be expressed completely by the proposed representation method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1101-1106
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• 2008

People who have hearing difficulties suffer from satisfactory mutual interaction with normal people because there are little chances of communicating each other. It is caused by rare communication of people who have hearing difficulties with normal people because majority of normal people can not understand sing language that is represented by gestures and is used by people who have hearing difficulties as a principal way of communication. In this paper, we propose a recognition method of finger language using FCM algorithm in order to be possible of communication of people who have hearing difficulties with normal people. In the proposed method, skin regions are extracted from images acquired by a camera using YCbCr and HSI color spaces and then locations of two hands are traced by applying 4-directional edge tracking algorithm on the extracted skin lesions. Final hand regions are extracted from the traced hand regions by noise removal using morphological information. The extracted final hand regions are classified and recognized by FCM algorithm. In the experiment using images of finger language acquired by a camera, we verified that the proposed method have the effect of extracting two hand regions and recognizing finger language.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1323-1329
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• 2021

For hand expression recognition, hand pose recognition based on the static shape of the hand and hand gesture recognition based on the dynamic hand movement are used together. In this paper, we propose a hierarchical hand pose model based on finger position and shape for hand expression recognition. For hand pose recognition, a finger model representing the finger state and a hand pose model using the finger state are hierarchically constructed, which is based on the open source MediaPipe. The finger model is also hierarchically constructed using the bending of one finger and the touch of two fingers. The proposed model can be used for various applications of transmitting information through hands, and its usefulness was verified by applying it to number recognition in sign language. The proposed model is expected to have various applications in the user interface of computers other than sign language recognition.

• 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.