• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.440-451
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• 2019

We propose a system that allows three dimensional manipulation of face in video. The 3D face manipulation of the proposed system overlays the 3D face model with the user 's manipulation on the face region of the video frame, and it allows 3D manipulation of the video in real time unlike existing applications or methods. To achieve this feature, first, the 3D morphable face model is registered with the image. At the same time, user's manipulation is applied to the registered model. Finally, the frame image mapped to the model as texture, and the texture-mapped and deformed model is rendered. Since this process requires lots of operations, parallel processing is adopted for real-time processing; the system is divided into modules according to functionalities, and each module runs in parallel on each thread. Experimental results show that specific parts of the face in video can be manipulated in real time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.8
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• pp.855-861
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• 2007

Haptic rendering is a process providing force feedback during interactions between a user and a virtual object. This paper presents a real-time haptic rendering technique for deformable objects based on visual information of intervention between a tool and a real object in a remote place. A user can feel the artificial reaction force through a haptic device in real-time when a slave system exerts manipulation tasks on a deformable object. The models of the deformable object and the manipulator are created from the captured image obtained with a CCD camera and the recognition of objects is achieved using image processing techniques. The force at a rate of 1 kHz for stable haptic interaction is deduced using extrapolation of forces at a low update rate. The rendering algorithm developed was tested and validated on a test platform consisting of a one-dimensional indentation device and an off-the shelf force feedback device. This software system can be used in a cellular manipulation system providing artificial force feedback to enhance a success rate of operations.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.250-253
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• 2000

In this paper, we Implement a new arithmetic computation for MPEG audio data to overcome the limitations of real number processing in the fixed-point arithmetics, such as: overheads in processing time and power consumption. We aims at efficiently dealing with real numbers by extending the fixed-point arithmetic manipulation for floating-point numbers in MPEG audio data, and implementing the DSP libraries to support the manipulation and computation of real numbers with the fixed-point resources.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.491-501
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• 2019

Variational methods, which cast deformation as an energy-minimization problem, are known to provide a good trade-off between practicality and speed. However, the time required to deform a fully detailed shape means that these methods are largely unsuitable for real-time applications. We simplify a 2D shape into a curve skeleton, which can be deformed much more rapidly than the original shape. The curve skeleton also provides a simplified control for the user, utilizing a small number of control handles. Our system deforms the curve skeleton using an energy-minimization method and then applies the resulting deformation to the original shape using linear blend skinning. This approach effectively reduces the size of the variational optimization problem while producing deformations of a similar quality to those obtained from full-scale nonlinear variational methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2731-2736
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• 2003

In highly informative, perception-rich environments that we call Omniscient Spaces, robots interact with physical objects which in turn afford robots the information showing how the objects should be manipulated. Object manipulation is commonly believed one of the most basic tasks in robot applications. However, no approaches including visual servoing seem satisfactory in unstructured environments such as our everyday life. Thus, in Omniscient Spaces, the features of the environments embed themselves in every entity, allowing robots to easily identify and manipulate unknown objects. To achieve this end, we propose a new paradigm of the interaction through Radio Frequency Identification (RFID). The aim of this paper is to learn about RFID and investigate how it works in object manipulation. Specifically, as an innovative trial for autonomous, real-time manipulation, a likely mobile robot equipped with an RFID system is developed. Details on the experiments are described together with some preliminary results.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korea Computer Graphics Society
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• v.10 no.1
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• pp.15-21
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• 2004

This paper proposes a real-time technique for simulating large rotational deformations. Modal analysis based on a linear strain tensor has been shown to be suitable for real-time simulation, but is accurate only for moderately small deformations. In the present work, we identify the rotational component of an infinitesimal deformation, and extend linear modal analysis to track that component. We then develop a procedure to integrate the small rotations occurring al the nodal points. An interesting feature of our formulation is that it can implement both position and orientation constraints in a straightforward manner. These constraints can be used to interactively manipulate the shape of a deformable solid by dragging/twisting a set of nodes, Experiments show that the proposed technique runs in real-time even for a complex model, and that it can simulate large bending and/or twisting deformations with acceptable realism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.229-232
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• 2000

This research aims at developing a low-cost PC-NC system based on one-CPU and investigating the feasibility of its application to a simple-function lathe. Its hardware consists a two axes motion control board including a 24bit counter, 8253 timer, a 12bit DA converter, DIO board for PLC operation and a PC with Intel Pentium 466MHz. The fundamental real-time MC functions such as G-code interpretation, interpolation, position and velocity control of axes are performed. User programming interface with functions of icon manipulation, tool-path simulation and NC-code generation was implemented. In order to achieve real-time control and safety, axis control, NC interpretation, interpolation and user communication are completely executed during every interrupt interval of I msec.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.