• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3924-3940
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• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• Spatial Information Research
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• v.3 no.2
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• pp.135-146
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• 1995

The purpose of this study is to develop a GIS application and interface model (GISCELWAB) for the temporal and spatial simulation of surface runoff from a small watershed. The model was constituted by three sub - models : The input data extraction model (GISINDATA) which prepares cell-based input data automatically for a given watershed, the cell water balance model(CELWAB) which calculates the water balance for a cell and simulates surface runoff of watershed simultaneously by the interaction of cells, and the output data management model(GISOUTDISP) which visualize the results of temporal and spatial variation of surface runoff. The input data extraction model was developed to solve the time-consuming problems for the input-data preparation of distributed hydrologic model. The input data for CELWAB can be obtained by extracting ASCII data from a vector map. The output data management model was developed to convert the storage depth and discharge of cell into grid map. This model ean-bles to visualize the temporal and spatial formulation process of watershed storage depth and surface runoff wholly with time increment.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.73-77
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• 2012

This paper reports a combined depth- and model-based face detection and tracking approach. The proposed algorithm consists of four functional modules; i) color-based candidate region extraction, ii) generation of the depth histogram for handling occlusion, iii) rotation-invariant face region detection using ellipse fitting, and iv) face tracking based on motion prediction. This technique solved the occlusion problem under complicated environment by detecting the face candidate region based on the depth-based histogram and skin colors. The angle of rotation was estimated by the ellipse fitting method in the detected candidate regions. The face region was finally determined by inversely rotating the candidate regions by the estimated angle using Haar-like features that were robustly trained robustly by the frontal face.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.525-531
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• 2019

Efforts to utilize human gestures to effectively implement a more natural and interactive interface between humans and computers have been ongoing in recent years. In this paper, we propose a new algorithm that accepts consecutive three-dimensional (3D) depth images, defines a hand model, and robustly extracts the human hand region based on six palm joints and 15 finger joints. Then, the 3D depth images are adaptively binarized to exclude non-interest areas, such as the background, and accurately extracts only the hand of the person, which is the area of interest. Experimental results show that the presented algorithm detects only the human hand region 2.4% more accurately than the existing method. The hand region extraction algorithm proposed in this paper is expected to be useful in various practical applications related to computer vision and image processing, such as gesture recognition, virtual reality implementation, 3D motion games, and sign recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.384-390
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• 2020

The reverberation effect on the sound when producing movies or VR contents is a very important factor in the realism and liveliness. The reverberation time depending the space is recommended in a standard called RT60(Reverberation Time 60 dB). In this paper, we propose a scene recognition technique for automatic reverberation editing. To this end, we devised a classification model that independently trains color images and predicted depth images in the same model. Indoor scene classification is limited only by training color information because of the similarity of internal structure. Deep learning based depth information extraction technology is used to use spatial depth information. Based on RT60, 10 scene classes were constructed and model training and evaluation were conducted. Finally, the proposed SCR + DNet (Scene Classification for Reverb + Depth Net) classifier achieves higher performance than conventional CNN classifiers with 92.4% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.149-158
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• 2008

The parameters of each basin, required for the accurate analysis of flood runoff using Storage Function Model, are estimated. Prior to the estimation, sensitivity analysis and extraction of new regional topographic factors for Han River basin are conducted. Based on the result, the outflow constant of basin model is calculated through regression analysis in relation with pre-flood runoff depth. The storage constant of basin model is derived by the optimum storage constant equation, according to the flood event of each basin. The model using the mentioned parameters was compared with K-Water model of Korea Water Resources Corporation and the model of Han River Flood Control Office, and proved to correspond to the observed hydrograph more.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.50-62
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• 1996

A parallel looking stereo camera was mainly used as an input sensor for digital image processing, image understanding and the extraction of 3 dimensional information. Theoretical analysis and performance evaluation are dealt in this paper for a convergent looking stereo camera model having a fixation point with the result of crossing optical axes. The quantization error, depth resolution and equidepth map due to digital pixels, and the misalignments effects of pan, tilt and roll angles are analyzed by using rhe relationship between the reference and image coordinate systems. Also horopter, epipolar lines, probability density functions of the depth error, and stereo fusion areas for the two camera models are discussed.

• Journal of the Optical Society of Korea
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• v.17 no.5
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• pp.410-414
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• 2013

In this paper, we propose a 3D display method combining a pickup process using axially recorded multiple images and an integral imaging display process. First, we extract the color and depth information of 3D objects for displaying 3D images from axially recorded multiple 2D images. Next, using the extracted depth map and color images, elemental images are computationally synthesized based on a ray mapping model between 3D space and an elemental image plane. Finally, we display 3D images optically by an integral imaging system with a lenslet array. To show the usefulness of the proposed system, we carry out optical experiments for 3D objects and present the experimental results.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.367-380
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• 2024

In this study, a prediction model for the cutting force evolution in brittle rocks was developed. This model is based on indentation tests using a conical pick at a cutting depth of 9 mm. The behavior of the indentation mechanism was analyzed in three phases by using Evans' cutting mode. The peak values in the force history identified these phases. The variation in the local strength of the rock caused a large offset in the model prediction of chipping. Regression analyses showed that there is a strong power relationship between the upper bound of the cutting force along with chipping and depth of cut. The slope of the three crushing phases has been found to increase sequentially (α₁<α₂<α₃). In addition, a positive correlation existed between the Schmidt hardness and brittleness index that affects the lower and upper bounds of chipping. Consequently, the results clearly demonstrate that the new model can reasonably predict the evolution of the cutting force based on experimental data. These results would be beneficial for engineers to design and select the optimum excavation machine to reduce mechanical vibration and enhance cutting efficiency.