• Title/Summary/Keyword: skeleton data

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Investigation of Thermal Characteristics and Skeleton Size Effects to improve Dimensional Accuracy of Variable Lamination Manufacturing by using EPS Foam (발포 폴리스티렌 폼을 이용한 가변적층 쾌속조형공정의 형상 정밀도 개선을 위한 열전달 특성 및 잔여 재료폭 영향에 관한 연구)

  • 안동규;이상호;양동열
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2001.04a
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    • pp.910-913
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    • 2001
  • Rapid Prototyping(RP) techniques have unique characteristics according to their working principle: the stair-stepped surface of a part due to layer-by-layer stacking, low building speed, and additional post-processing to improve surface roughness. A new RP process, Variable Lamination Manufacturing by using expandable polystyrene foam(VLM-S), has been developed to overcome the unfavorable characteristics. The objective of this study is to investigate the thermal characteristics and skeleton size effects as the hotwire cuts EPS foam sheet in order to improve dimensional accuracy of the parts, which is produced by VLM-S. Empirical and analytical approaches are performed to find the relationship between cutting speed and heat input, and the relationship between maximum available cutting speed and heat input. In addition, empirical approaches are carried out to find the relationship between cutting error and skeleton size, and cutting deviation and skeleton size. Based on these results, the optimal hotwire cutting condition and available minimum skeleton size are derived. The outcomes of this study are reflecting in the enhancement of VLM-S input data generation S/W.

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Rotation Invariant 3D Star Skeleton Feature Extraction (회전무관 3D Star Skeleton 특징 추출)

  • Chun, Sung-Kuk;Hong, Kwang-Jin;Jung, Kee-Chul
    • Journal of KIISE:Software and Applications
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    • v.36 no.10
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    • pp.836-850
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    • 2009
  • Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

Realtime 3D Human Full-Body Convergence Motion Capture using a Kinect Sensor (Kinect Sensor를 이용한 실시간 3D 인체 전신 융합 모션 캡처)

  • Kim, Sung-Ho
    • Journal of Digital Convergence
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    • v.14 no.1
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    • pp.189-194
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    • 2016
  • Recently, there is increasing demand for image processing technology while activated the use of equipments such as camera, camcorder and CCTV. In particular, research and development related to 3D image technology using the depth camera such as Kinect sensor has been more activated. Kinect sensor is a high-performance camera that can acquire a 3D human skeleton structure via a RGB, skeleton and depth image in real-time frame-by-frame. In this paper, we develop a system. This system captures the motion of a 3D human skeleton structure using the Kinect sensor. And this system can be stored by selecting the motion file format as trc and bvh that is used for general purposes. The system also has a function that converts TRC motion captured format file into BVH format. Finally, this paper confirms visually through the motion capture data viewer that motion data captured using the Kinect sensor is captured correctly.

Interactive Typography System using Combined Corner and Contour Detection

  • Lim, Sooyeon;Kim, Sangwook
    • International Journal of Contents
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    • v.13 no.1
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    • pp.68-75
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    • 2017
  • Interactive Typography is a process where a user communicates by interacting with text and a moving factor. This research covers interactive typography using real-time response to a user's gesture. In order to form a language-independent system, preprocessing of entered text data presents image data. This preprocessing is followed by recognizing the image data and the setting interaction points. This is done using computer vision technology such as the Harris corner detector and contour detection. User interaction is achieved using skeleton information tracked by a depth camera. By synchronizing the user's skeleton information acquired by Kinect (a depth camera,) and the typography components (interaction points), all user gestures are linked with the typography in real time. An experiment was conducted, in both English and Korean, where users showed an 81% satisfaction level using an interactive typography system where text components showed discrete movements in accordance with the users' gestures. Through this experiment, it was possible to ascertain that sensibility varied depending on the size and the speed of the text and interactive alteration. The results show that interactive typography can potentially be an accurate communication tool, and not merely a uniform text transmission system.

Deep Learning-based Action Recognition using Skeleton Joints Mapping (스켈레톤 조인트 매핑을 이용한 딥 러닝 기반 행동 인식)

  • Tasnim, Nusrat;Baek, Joong-Hwan
    • Journal of Advanced Navigation Technology
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    • v.24 no.2
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    • pp.155-162
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    • 2020
  • Recently, with the development of computer vision and deep learning technology, research on human action recognition has been actively conducted for video analysis, video surveillance, interactive multimedia, and human machine interaction applications. Diverse techniques have been introduced for human action understanding and classification by many researchers using RGB image, depth image, skeleton and inertial data. However, skeleton-based action discrimination is still a challenging research topic for human machine-interaction. In this paper, we propose an end-to-end skeleton joints mapping of action for generating spatio-temporal image so-called dynamic image. Then, an efficient deep convolution neural network is devised to perform the classification among the action classes. We use publicly accessible UTD-MHAD skeleton dataset for evaluating the performance of the proposed method. As a result of the experiment, the proposed system shows better performance than the existing methods with high accuracy of 97.45%.

Research on Correlating Data Loading with User Experience (데이터 로딩과 사용자 경험의 상관관계 분석에 관한 연구)

  • In-sik Yun;Il-young Moon
    • Journal of Practical Engineering Education
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    • v.16 no.2
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    • pp.185-193
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    • 2024
  • With the advent of smartphones, people can access various information through the internet anytime and anywhere. Even in the vehicle environment, users can use the internet. Users interact with web and applications every day and get information. However, as the amount of data to be processed by the program increases, users inevitably receive a message to wait. User waiting is an inconvenient experience, but minimizing user waiting is the best way because there is time required for data processing. However, if the service processing time exceeds the expected time, users experience more severe boredom and pain. Therefore, various methods and researches are being conducted to alleviate the boredom of user waiting. The most commonly used method to alleviate user waiting boredom is loading. In this study, we investigated the effect of skeleton loading, the latest loading technique, on user waiting experience, and how attractive it is as a design technique in terms of UI compared to other loading techniques.

A Design and Implementation of Worker Motion 3D Visualization Module Based on Human Sensor

  • Sejong Lee
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.9
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    • pp.109-114
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    • 2024
  • In this paper, we design and implement a worker motion 3D visualization module based on human sensors. The three key modules that make up this system are Human Sensor Implementation, Data Set Creation, and Visualization. Human Sensor Implementation provides the functions of setting and installing the human sensor locations and collecting worker motion data through the human sensors. Data Set Creation offers functions for converting and storing motion data, creating near real-time worker motion data sets, and processing and managing sensor and motion data sets. Visualization provides functions for visualizing the worker's 3D model, evaluating motions, calculating loads, and managing large-scale data. In worker 3D model visualization, motion data sets (Skeleton & Position) are synchronized and mapped to the worker's 3D model, and the worker's 3D model motion animation is visualized by combining the worker's 3D model with analysis results. The human sensor-based worker motion 3D visualization module designed and implemented in this paper can be widely utilized as a foundational technology in the smart factory field in the future.

Osteological Development of the Larvae and Juvenile in Sakhalin Sole Limanda sakhalinensis (사할린가자미(Limanda sakhalinensis) 자치어의 골격발달)

  • Han, Kyeong-Ho;Lee, Seong-Hoon;Baek, Jeong-Ik;Park, Jae-Min
    • Korean Journal of Fisheries and Aquatic Sciences
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    • v.52 no.4
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    • pp.389-399
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    • 2019
  • This study is designed to observe the development of skeleton of Sakhalin sole Limanda sakhalinensis in order to use the findings as the basic data for the taxonomic research. As for the development of skeleton, on 20 days of the hatch when its average total length was 5.86 mm, the clavicle and the paraspenoid in the cranium were ossified. As for the jaw bone, the premaxillary and the dentary were ossified. On 28 days of hatch when its total length was 7.05 mm, 25 neural spines and 22 hemal spines were ossified and 5 fin rays appeared. On 34 days of hatch when its average total length was 8.40 mm, the end of tail was twisted by $45^{\circ}$ and 3 hypural bones were ossified. On 48 days of hatch when its average total length was 10.1 mm, 2 actinosts below the postcleithrum were ossified. On 54 days of hatch, when the average total length was 10.4 mm, the ethmoid, prootic and exoccipital were ossified, thus completing the ossification of skeleton.

A Hardware Architecture for Retaining the Connectivity in Gray - Scale Image (그레이 레벨 연결성 복원 하드웨어 구조)

  • 김성훈;양영일
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.974-977
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    • 1999
  • In this paper, we have proposed the hardware architecture which implements the algorithm for retaining the connectivity which prevents disconnecting in the gray-scale image thinning To perform the image thinning in a real time which find a skeleton in image, it is necessary to examine the connectivity of the skeleton in a real time. The proposed architecture finds the connectivity number in the 4-clock period. The architecture is consists of three blocks, PS(Parallel to Serial) Converter and State Generator and Ridge Checker. The PS Converter changes the 3$\times$3 gray level image to four sets of image pixels. The State Generator examine the connectivity of the central pixel by searching the data from the PS Converter. the 3$\times$3 gray level image determines. The Ridge Checker determines whether the central pixel is on the skeleton or not The proposed architecture finds the connectivity of the central pixel in a 3$\times$3 gray level image in the 4-clocks. The total circuits are verified by the design tools and operate correctly.

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Motion Recognition of Workers using Skeleton and LSTM (Skeleton 정보와 LSTM을 이용한 작업자 동작인식)

  • Jeon, Wang Su;Rhee, Sang Yong
    • Journal of Korea Multimedia Society
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    • v.25 no.4
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    • pp.575-582
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    • 2022
  • In the manufacturing environment, research to minimize robot collisions with human beings have been widespread, but in order to interact with robots, it is important to precisely recognize and predict human actions. In this research, after enhancing performance by applying group normalization to the Hourglass model to detect the operator motion, the skeleton was estimated and data were created using this model. And then, three types of operator's movements were recognized using LSTM. As results of the experiment, the accuracy was enhanced by 1% using group normalization, and the recognition accuracy was 99.6%.