• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.142-159
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• 1996

Engineering of automated tools for the agro-food industries and the rural world activities have to pick up two challenges : to answer the immediate important problems related to the situation of these industries, and to imaging the tools that their professional will need next century. Creating or modifying automated tools in the next few will be made taking into account parameters either technical (environmental protection, health and safety), or social and economical (investment , employment). There will be a strong interaction with disciplines like ecology, medicine, ergonomy, psycho-sociology , etc. , The partners for such a research, tools manufactures and users, should have an early involvement in its content, in order to find rapidly the solution to the drastic problems they are meeting. On a longer term , during the next 20 years , there will be an important evolution of the rural space management and of the food processes. This will imply the emergence of new types of activities and know-how's , with lines of automated tools to be invented and developed , like : micro-system for organic localized tasks -mobile and adaptive equipments highly autonomous for natural space actions - device for perception , decision and control reproducing automatically the expert behaviors of human operators. Design of such automated tools need to overcome technological difficulties like the automation of the expert-decision process, or the management of complex design.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.186-191
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• 2015

Hand gesture is the most common tool used to interact with and control various electronic devices. In this paper, we propose a novel hand gesture recognition method using fuzzy logic based classification with a new type of sensor array. In some cases, feature patterns of hand gesture signals cannot be uniquely distinguished and recognized when people perform the same gesture in different ways. Moreover, differences in the hand shape and skeletal articulation of the arm influence to the process. Manifold features were extracted, and efficient features, which make gestures distinguishable, were selected. However, there exist similar feature patterns across different hand gestures, and fuzzy logic is applied to classify them. Fuzzy rules are defined based on the many feature patterns of the input signal. An adaptive neural fuzzy inference system was used to generate fuzzy rules automatically for classifying hand gestures using low number of feature patterns as input. In addition, emotion expression was conducted after the hand gesture recognition for resultant human-robot interaction. Our proposed method was tested with many hand gesture datasets and validated with different evaluation metrics. Experimental results show that our method detects more hand gestures as compared to the other existing methods with robust hand gesture recognition and corresponding emotion expressions, in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1977-1985
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• 2007

The character recognition technique is one of the artificial intelligence and has been widely applied in the automated system robot HCI(Human Computer Interaction), etc. This paper introduces the character set and the representative character that can be used in the recognition of the mage ROI. The character codes in this ROI include the digit, symbol, English and Hereat etc. We proposed the efficient multi-classifier structure by combining the small-size classifiers hierarchically. Moreover, we generated each small-size classifiers by delta-bar-delta learning algorithm. We tested the performance with various kinds of images and achieved the accuracy of 99%. The proposed multi-classifier showed the efficiency and the reliability for the mixed character code set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.73-75
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• 2022

Scene Text Recognition is a technology used in the field of artificial intelligence that requires manless robot, automatic vehicles and human-computer interaction. Though scene text images are distorted by noise interference, such as illumination, low resolution and blurring. Unlike previous studies that recognized only English, this paper shows a strong recognition accuracy including various characters, English, Korean, special character and numbers. Instead of selecting only one class having the highest probability value, a candidate word can be generated by considering the probability value of the second rank as well, thus a method can be corrected an existing language misrecognition problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.429-431
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• 2021

Recently, as an alternative to replace face-to-face meetings, video conferencing systems between remote locations has increased. However, video conferencing systems have limitations in terms of mismatch of the eyes of remote users. Therefore, it is necessary to apply a technology that can increase the level of immersion in video conferences by matching the gaze information of participants between different remote locations. In this paper, we propose a novel technique to realize video conferencing with the same gaze by estimating the speaker's location based on a multi-microphone. Using our method, it can be applied to various fields such as robot interaction and virtual human interface as well as video conferencing between remote locations.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.1
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• pp.71-76
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• 2018

The technology convergence of television, communication, and computing devices enables the rich social and entertaining experience through Smart TV in personal living space. The powerful smart TV computing platform allows to provide various user interaction interfaces like IR remote control, web based control, body gesture based control, etc. The presently used smart TV interaction user control methods are not efficient and user-friendly to access different type of media content and services and strongly required advanced way to control and access to the smart TV with easy user interface. This paper propose the optical wireless communication (OWC) based remote controller design for Smart TV using smart device Flashlights. In this approach, the user smart device act as a remote controller with touch based interactive smart device application and transfer the user control interface data to smart TV trough Flashlight using visible light communication method. The smart TV built-in camera follows the optical camera communication (OCC) principle to decode data and control smart TV user access functions according. This proposed method is not harmful as radio frequency (RF) radiation does it on human health and very simple to use as well user does need to any gesture moves to control the smart TV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.131-147
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• 2020

RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of K best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.1-7
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• 2013

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.112-118
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• 2009

Skin color segmentation techniques have been widely utilized for face/hand detection and tracking in many applications such as a diagnosis system using facial information, human-robot interaction, an image retrieval system. In case of a video image, it is common that the skin color model for a target is updated every frame for the robust target tracking against illumination change. As for a single image, however, most of studies employ a fixed skin color model which may result in low detection rate or high false positive errors. In this paper, we propose a novel method for effective skin color segmentation in a single image, which modifies the conditions for skin color segmentation iteratively by the image feedback of segmented skin color region in a given image.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.622-627
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• 2018

In this paper, we introduce an integrated augmented reality system using a small camera and a projector. We extract three-dimensional information of an object with a small portable camera and a projector by using a structured light system. We develop the concept of the virtual camera to generalize the projection method so that the image can be projected at a desired position with only the mesh of the object to be projected without computing the mapping between specific point sets. Therefore, it is possible to project not only simple planes but also complex curved surfaces to desired positions without complicated geometric calculation. Based on a robot with a small camera and a projector, it will largely explain the projector-camera system calibration, the calculation of the position of the recognized object, and the image projection method using the virtual camera concept.