• Journal of Multimedia Information System
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• v.6 no.4
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• pp.329-332
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• 2019

Recently, smart lighting systems are the combination between sensors and lights. These systems turn on/off and adjust the brightness of lights based on the motion of object and the brightness of environment. These systems are often applied in places such as buildings, rooms, garages and parking lot. However, these lighting systems are controlled by lighting sensors, motion sensors based on illumination environment and motion detection. In this paper, we propose an automatic lighting control system using one single camera for buildings, rooms and garages. The proposed system is one integration the results of digital image processing as motion detection, hand gesture detection to control and dim the lighting system. The experimental results showed that the proposed system work very well and could consider to apply for automatic lighting spaces.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.411-417
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• 2021

The field that studies human-computer interaction is called HCI (Human-computer interaction). This field is an academic field that studies how humans and computers communicate with each other and recognize information. This study is a study on hand gesture recognition for human interaction. This study examines the problems of existing recognition methods and proposes an algorithm to improve the recognition rate. The hand region is extracted based on skin color information for the image containing the shape of the human hand, and the center of gravity profile is calculated using principal component analysis. I proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. We proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. The existing center of gravity profile has shown the result of incorrect hand gesture recognition for the deformation of the hand due to rotation, but in this study, the center of gravity profile is used and the point where the distance between the points of all contours and the center of gravity is the longest is the starting point. Thus, a robust algorithm was proposed by re-improving the center of gravity profile. No gloves or special markers attached to the sensor are used for hand gesture recognition, and a separate blue screen is not installed. For this result, find the feature vector at the nearest distance to solve the misrecognition, and obtain an appropriate threshold to distinguish between success and failure.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.473-480
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• 2014

As the use of computer has been popularized these days, an active research is in progress to make much more convenient and natural interface compared to the existing user interfaces such as keyboard or mouse. For this reason, there is an increasing interest toward Microsoft's motion sensing module called Kinect, which can perform hand motions and speech recognition system in order to realize communication between people. Kinect uses its built-in sensor to recognize the main joint movements and depth of the body. It can also provide a simple speech recognition through the built-in microphone. In this paper, the goal is to use Kinect's depth value data, skeleton tracking and labeling algorithm to recognize information about the extraction and movement of hand, and replace the role of existing peripherals using a virtual mouse, a virtual keyboard, and a speech recognition.

• Journal of Biosystems Engineering
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• v.22 no.4
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• pp.411-420
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• 1997

The mechanization efficiency using high ability machines such as tractors or combines in a paddy field rice farm is high. Mechanization in harvesting fruits and vegetables is difficult, because they are easy to be damaged. Therefore, Advanced techniques for careful handling fruits and vegetables are necessary in automation and robotization. An apple harvesting robot must have a recognition device to detect the positioning of fruit, manipulators which function like human arms, and hand to take off the fruit. This study is related to the development of a rotatic hand as the first stage in developing the apple harvesting robot. The results are summarized as follows. 1. It was found that a hand that was eccentric in rotatory motion, was better than a hand of semicircular up-and-down motion in harvesting efficiency. 2. The hand was developed to control changes in grasp forces by using tape-type switch sensor which was attatched to fingers' inside. 3. Initial finger positioning was set up to control accurate harvesting by using a tow step fingering position. 4. This study showed the possibility of apple harvesting using the developed robot hand.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.72-75
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• 2004

In this paper, a finger-click recognition method is proposed to improve the recognition performance for finger-clicking of a wearable input device, called $SCURRY^{TM}$. The proposed method is composed of three parts including feature extraction part, valid click discrimination part, and cross-talk avoidance part. Two types of MEMS inertial sensors are embedded into the wearable input device to measure the angular velocity of a hand (hand movement) and the acceleration rates at the ends of fingers (finger-click motion). The experiment applied to the $SCURRY^{TM}$ device shows the improved stability and performance.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• 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 Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1071-1076
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• 2006

In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages; the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. The experimental results indicate that our proposed method is able to decrease the prediction error up to 96.87% with negligible increase in computational complexity of up to 4%.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.639-645
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• 2012

In this paper, we propose a method that gives motion command to a mobile robot to recognize human being's hand gesture. Former way of the robot-controlling system with the movement of hand used several kinds of pre-arranged gesture, therefore the ordering motion was unnatural. Also it forced people to study the pre-arranged gesture, making it more inconvenient. To solve this problem, there are many researches going on trying to figure out another way to make the machine to recognize the movement of the hand. In this paper, we used third-dimensional camera to obtain the color and depth data, which can be used to search the human hand and recognize its movement based on it. We used HMM method to make the proposed system to perceive the movement, then the observed data transfers to the robot making it to move at the direction where we want it to be.