• The Journal of Korean Institute of Next Generation Computing
• /
• v.14 no.6
• /
• pp.66-74
• /
• 2018

Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.

• The Journal of the Korea Contents Association
• /
• v.10 no.12
• /
• pp.51-58
• /
• 2010

In the environment of instant messengers, the recognition of human emotions and its automated representation with personalized 3D character animations facilitate the use of affectivity in the machine-based communication, which will contribute to enhanced communication. This paper describes an emotional messenger system developed for the automated recognition and expression of emotions for IPTVs (Internet Protocol televisions). Aiming for efficient delivery of users' emotions, we propose emotion estimation that assesses the affective contents of given textual messages, character animation that supports both 3D rendering and video playback, and smart phone-based input method. Demonstration and experiments validate the usefulness and performance of the proposed system.

• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.11 s.188
• /
• pp.58-67
• /
• 2006

Due to the variety of signal processing and complicated mathematical analysis, it is not easy to accomplish 3D bin-picking with non-contact sensor. To solve this difficulties the reliable signal processing algorithm and a good sensing device has been recommended. In this research, 3D laser scanner and CCD camera is applied as a sensing device respectively. With these sensor we develop a two-step bin-picking method and reliable algorithm for the recognition of 3D bin object. In the proposed bin-picking, the problem is reduced to 2D intial recognition with CCD camera at first, and then 3D pose detection with a laser scanner. To get a good movement in the robot base frame, the hand eye calibration between robot's end effector and sensing device should be also carried out. In this paper, we examine auto-calibration technique in the sensor calibration step. A new thinning algorithm and constrained hough transform is also studied for the robustness in the real environment usage. From the experimental results, we could see the robust bin-picking operation under the non-aligned 3D hole object.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.59-66
• /
• 2021

Humans mainly recognize surrounding objects using visual and auditory information among the five senses (sight, hearing, smell, touch, taste). Major research related to the latest object recognition mainly focuses on analysis using image sensor information. In this paper, after emitting various chirp audio signals into the observation space, collecting echoes through a 2-channel receiving sensor, converting them into spectral images, an object recognition experiment in 3D space was conducted using an image learning algorithm based on deep learning. Through this experiment, the experiment was conducted in a situation where there is noise and echo generated in a general indoor environment, not in the ideal condition of an anechoic room, and the object recognition through echo was able to estimate the position of the object with 83% accuracy. In addition, it was possible to obtain visual information through sound through learning of 3D sound by mapping the inference result to the observation space and the 3D sound spatial signal and outputting it as sound. This means that the use of various echo information along with image information is required for object recognition research, and it is thought that this technology can be used for augmented reality through 3D sound.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1994.04a
• /
• pp.703-712
• /
• 1994

This paper presents an automatic conversion of machining data from the orthographic views of press mold by feature recognition rule. The system includes following 6 modules : separation of views, function support, dimension text recognition, feature recognition, dimension text check and feature processing modules. The characteristic of this system is that with minimum user intervention, it recognizes basic features such as holes, slots, pockets and clamping parts and thus automatically converts CAD drawing details of press mold into machining data using 2D CAD system instead of using an expensive 3D Modeler. The system is developed by using IBM-PC in the environment of AutoCAD R12, AutoLISP and MetaWare High C. Performance of the system is verified as a good interfacing of CAD and CAM when applied to a lot of sample drawings.

• Proceedings of the KAIS Fall Conference
• /
• 2007.05a
• /
• pp.195-198
• /
• 2007

본 논문에서는 유아를 대상으로 하는 환경교육용 3D 애니메이션을 제작하여, 유아에게 환경오염의 심각성을 알리고, 유치원에서 환경교육 자료로 활용될 수 있도록 환경교육의 실천 방향을 제시한다. 유치원에서 유아를 대상으로 사전조사와 사후 조사를 통하여 유아들의 이해도가 향상되었음을 결과를 통하여 알 수 있다.

• Proceedings of the KAIS Fall Conference
• /
• 2010.05a
• /
• pp.511-514
• /
• 2010

본 논문에서는 유아를 대상으로 하는 환경교육용 3D 애니메이션을 제작하여, 유아에게 환경오염의 심각성을 알리고, 유치원에서 환경교육 자료로 활용될 수 있도록 환경교육의 실천 방향을 제시한다. 유치원에서 유아를 대상으로 사전조사와 사후 조사를 통하여 유아들의 이해도가 향상되었음을 결과를 통하여 알 수 있다.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.3
• /
• pp.1-9
• /
• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.659-666
• /
• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.6
• /
• pp.678-682
• /
• 2021

Reverberation Time (RT) with noise levels can affect speech recognition ability in a listening environment. The degree of influence may depend on reverberation times and modes of binaural hearing. In this study, Korean monosyllabic Word Recognition Scores (WRS) were investigated in 10 young normal hearing subjects under binaural conditions. The RT of 3.4 s and signal to noise ratio of 0 dB were used at 55 dB HL for diotic (noise with the same phase) and dichotic (noise with the fixed phase difference, π) conditions. The improvement in WRS was noted in dichotic hearing (p < 0.05) while the similar trend was not observed in diotic hearing. This data may be useful in analyzing psychoacoustic effects of RTs under noisy conditions.