• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.708-711
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• 2008

This study is intended to build research for ways to utilize material information in the design and working business for public works. The contents and results of this study can be classified into object-oriented DB application to bridge construction and object-oriented DB utilization of civil material information. First, application of object-oriented DB to bridge construction 1) constructs the work unit of classified work table as an object(Each object constructs material information on the statement of quantity calculation as data), 2) constructs object-oriented DB for superstructure and substructure of PSC Beam bridge, 3) leads to the research for ways to utilize materials by developing 3D bridge prototype with REVIT structure. Secondly, ways to utilize object-oriented DB for civil material information identified the possibility for utilizing it in making 2D drawings for design work, preparing materials list, analyzing structure for working businesses, selecting and purchasing materials, managing process and maintaining. It is suggested that the results of this study should be applied to all bridge constructions through test-bed and additional studies so as to secure the credibility of the results of this study.

• Proceedings of the IEEK Conference
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• summer
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• pp.361-364
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• 2004

This paper describes an automatic 3D models generation algorithm based on 2D silhouette images, using X-ray camera without camera parameters. The algorithm takes a multi steps process approach. First, a series of 2D silhouette images is captured from different directions of object and then converted to binary images. An octree data structure is constructed for voxel-based representation of object. An estimate 3D volume of object can be reconstructed by intersecting voxels and the 2D silhouettes. The marching cube algorithm is applied to get triangle mesh representing of the obtained 3D model for rendering.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.248-254
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• 2001

This paper presents a progressive algorithm for reconstructing a 3D structure from a given 2D sketch drawing (edge-vertex graph without hidden line removal) according to the user's sketch order. While previous methods reconstruct a 3D structure at once, the proposed method progressively calculate a 3D structure by optimizing the coordinates of vertices of an object according to the sketch order. The progressive method reconstructs the most plausible 3D object quickly by applying 3D constraints that are derived from the relationship between the object and the sketch drawing in the optimization process. The progressive reconstruction algorithm is discussed, and examples from a working implementation are given.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.329-332
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• 2023

3D Object Detection using deep neural network has been developed a lot for obstacle detection in autonomous vehicles because it can recognize not only the class of target object but also the distance from the object. But in the case of 3D Object Detection models, the detection performance for distant objects is lower than that for nearby objects, which is a critical issue for autonomous vehicles. In this paper, we introduce a technique that increases the performance of 3D object detection models, particularly in recognizing distant objects, by generating virtual 3D vehicle data and adding it to the dataset used for model training. We used a spherical point tracing method that leverages the characteristics of 3D LiDAR sensor data to create virtual vehicles that closely resemble real ones, and we demonstrated the validity of the virtual data by using it to improve recognition performance for objects at all distances in model training.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.511-513
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• 2001

본 연구에서는 VRML 저작 툴을 이용한 3차원 가상 전시공간을 구성하여 보다 현실감 있고 상호작용적인 학습공간을 제공하고 학습동기를 유발하는 체험학습 공간을 설계하고 구현하는데 초점을 맞췄다. 본 연구에서는 Photo Vista, Object Modeler, Reality Studio 등을 가지고 Panorama Image와 3D Object 등을 구현하는 방법을 이용하였다. 우리는 이 가상 전시공간이 학생들의 미술교육에서 현실감 있는 작품감상으로 학생들의 심미적인 면에 얼마나 많은 영향을 미치는지를 연구하고자 한다.

• Journal of Sensor Science and Technology
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• v.5 no.4
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• pp.57-70
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• 1996

Motion can be estimated continuously from each sensor through the analysis of the instantaneous states of an object. This paper is aimed to introduce a method to estimate the general 3D motion of a planar object from the instantaneous states of an object using multi-sensor data fusion. The instantaneous states of an object is estimated using the linear feedback estimation algorithm. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown planar object. We present a fusion algorithm which combines averaging and deciding. With the assumption that the motion is smooth, the approach can handle the data sequences from multiple sensors with different sampling times. Simulation results show proposed algorithm is advantageous in terms of accuracy, speed, and versatility.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.411-418
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• 2018

This paper presents a novel three-dimensional (3D) object encryption scheme by combining the use of the virtual optics and the chaotic sequence. A virtual 3D object is digitally produced using a two-dimensional (2D) elemental image array (EIA) created with a virtual pinhole array. Then, through a logistic mapping of chaotic sequence, a final encrypted video can be produced. Such method converts the value of a pixel which is the basic information of an image. Therefore, it gives an improved encryption result compared to other existing methods. Through computational experiments, we were able to verify our method's feasibility and effectiveness.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.71-81
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• 2012

In order to support simple authoring an application of augmented reality for diverse users, the process for the object registration comprised of tracking and recognizing the object should be accomplished intuitively and simple. Although many 3D reconstruction methods to be applied to the object registration have been developed, the methods have not beyond the experimental level yet. In this paper, we proposed a novel marker-based tangible interfaces for various users to manipulate the object with intuitive and simple approaches during an authoring applications fo augmented reality. The proposed method make use of marker as intuitive interface to obtain 3D geometric information of 3D reconstruction. 3D geometric information of an object surface is acquired by touching the object directly with the proposed tangible interfaces. The tangible interfaces not only support 3D reconstruction for graphical modeling but also offer features information which is used for augmented reality. Finally, we verify efficiency of the proposed method with demonstration of an augmented reality application using the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.509-516
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• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.