• Journal of Information Display
• /
• v.10 no.1
• /
• pp.6-15
• /
• 2009

In this paper, a new approach is proposed for the efficient generation of computer-generated holograms (CGHs) using the spatially redundant data on a 3D object and the novel look-up table (N-LUT) method. First, the pre-calculated N-point principle fringe patterns (PFPs) were calculated using the 1-point PFP of the N-LUT. Second, spatially redundant data on a 3D object were extracted and re-grouped into the N-point redundancy map using the run-length encoding (RLE) method. Then CGH patterns were generated using the spatial redundancy map and the N-LUT method. Finally, the generated hologram patterns were reconstructed. In this approach, the object points that were involved in the calculation of the CGH patterns were dramatically reduced, due to which the computational speed was increased. Some experiments with a test 3D object were carried out and the results were compared with those of conventional methods.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.10
• /
• pp.76-88
• /
• 1997

Based on the principles described in Part I, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.108-113
• /
• 2008

Virtual construction (VC) system enables project manager to visually check mistakes in design materials by using virtual reality technology. In using VC system, to make 3D object by each construction element is still tedious work. This study suggests an improved method to make 3D object by using parameter-based 3D generation function. The IDEFO model to organize the process for the function. A VC system by this function was developed in this study and the function was verified by a bridge project in this system.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.5
• /
• pp.392-399
• /
• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.

• The KIPS Transactions:PartA
• /
• v.11A no.2
• /
• pp.207-212
• /
• 2004

In this paper, based on SCORM Sequencing Model, we propose the learning content structure which has structure informations of learning object and decision rules how to transfer learning object to learner. It is intended to provide the technical means for learning content objects to be easily shared and reused across multiple learning delivery environment. We develop the generation tool of learning object sequencing, for processing the learning with variable teaching methodologies. The teaming objects also are automatically packaged the PIE(Package Interchange File) to transmit with SCORM RTE(Run-Time Environment) and attached SCO(Sharable Content Object) function for tracking learner information.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.108-116
• /
• 2008

In this paper, we propose the algorithm for making panoramic background and object tacking using pan-tilt-zoom camera. We draw an analogy relation between images for cylinder projection, rearrange of images, stitching, and blending. We can then make the panoramic background, and can track the object use the panoramic background. After generated the background, the proposed algorithm tracks the moving object. Therefore it can detect the wide area, and it tracks the object continuously. So the proposed algorithm is able to use at wide area to detect and track the object.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.3
• /
• pp.804-811
• /
• 1999

The object-oriented methods are being rapidly accepted as the solution for the software crisis. Object-oriented systems are composed of the integration of independent object modules; their merits are such that it is possible to reuse objects already developed, and that, when changes are required, modifications are restricted to some independent objects such that their affects to other objects are so little. In this paper, we deal with the macro assembler for next-generation microprocessors which has the merits of object methods. Whenever a microprocessor is newly developed, new assembler should be developed or the existing assembler be modified. In the former, it leads to the loss of time and money by the repeated developments, and, in the latter, it causes the problem of inefficient productivity since other modules are to be analyzed for the affections followed by modifications of one module, especially in the existing assemblers. To resolve these problems, the object-oriented macro assembler suggested in this paper consists of independent objects separable such that it shows reusability and reduces the inefficient productivity by minimizing the affects to other objects. Moreover, the object-oriented macro assembler integrates a macro pre-processor into an assembler, and uses automata for analyzing input streams to reduce the compile time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.44-46
• /
• 2022

Various object recognition using artificial intelligence basically shows planar results. It is based on classifying objects or identifying what objects are on the image. However, the original object has a three-dimensional shape, not a plane, and although the perception to obtain only simple results from the image does not matter, there is a lot of information that is insufficient when used in various fields. In this paper, checks the method of generating data in various fields of objects and whether it is meaningful by utilizing the characteristics of Layer that generates intermediate results with respect to image generation based on the GAN algorithm. It solves some of the problems in the hardware and collection process for generating existing multi-faceted data, and confirms that it can be utilized after data generation on several limited objects.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.32 no.1
• /
• pp.70-77
• /
• 2008

A pre-processing program based on the OOP(object-oriented programming) concept has been developed. The program consists of the input of a 2D or 3D flow problem to a CFD program by means of an user-friendly interface and the subsequent transformation of this input into a form suitable for the solver(PowerCFD) using unstructured cell-centered method. User-friendly GUI(graphic user interface) has been built on the base of MFC(Microsoft Foundation Class). The program is organized as modules by classes based on VTK(Visualization ToolKit)-library, and these classes are made to function through inheritance and cooperation which is an important and valuable concept of object-oriented programming. The major functions of this program are introduced and demonstrated, which include mesh generation, boundary settings, solver settings, generation of grid connectivity and geometric data etc.

• The Journal of Korea Robotics Society
• /
• v.14 no.1
• /
• pp.31-39
• /
• 2019

A robot usually adopts ANN (artificial neural network)-based object detection and instance segmentation algorithms to recognize objects but creating datasets for these algorithms requires high labeling costs because the dataset should be manually labeled. In order to lower the labeling cost, a new scheme is proposed that can automatically generate a training images and label them for specific objects. This scheme uses an instance segmentation algorithm trained to give the masks of unknown objects, so that they can be obtained in a simple environment. The RGB images of objects can be obtained by using these masks, and it is necessary to label the classes of objects through a human supervision. After obtaining object images, they are synthesized with various background images to create new images. Labeling the synthesized images is performed automatically using the masks and previously input object classes. In addition, human intervention is further reduced by using the robot arm to collect object images. The experiments show that the performance of instance segmentation trained through the proposed method is equivalent to that of the real dataset and that the time required to generate the dataset can be significantly reduced.