• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.93-100
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• 2014

3D object recognition using only 2D images is a difficult work because each images are generated different to according to the view direction of cameras. Because SIFT algorithm defines the local features of the projected images, recognition result is particularly limited in case of input images with strong perspective transformation. In this paper, we propose the object recognition method that improves SIFT algorithm by using several sequential images captured from rotating 3D object around a rotation axis. We use the geometric relationship between adjacent images and merge several images into a generated feature space during recognizing object. To clarify effectiveness of the proposed algorithm, we keep constantly the camera position and illumination conditions. This method can recognize the appearance of 3D objects that previous approach can not recognize with usually SIFT algorithm.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.149-152
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• 2000

3차원 물체 형상 모델링은 인식에 있어서 중요한 역할을 차지하고 있다. 기존의 픽셀(pixel)기반 영상표현은 물체 고유의 유기적 구조를 반영할 수 없고, 에지(edge)나 기반 물체 표현법은 물체의 자세한 표현이 가능하지만 물체인식을 위해서는 많은 양의 속성들을 만들어내게된다. 따라서 물체인식을 위해서는 물체의 형상특징을 직선적으로 기술할 수 있는 체적소 기반 물체 표현 방법이 필요하다. 본 논문에서는 몇 개의 파리미터를 이용하여 3차원 정보를 효과적으로 얻을 수 있는 superquadric과 이를 기본 단위로 한 CSG(Constructive Solid Geometry) tree를 이용하여 3 차원 물체 형상모델링에 대해서 기술한다.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.621-628
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• 2003

This paper addresses a method of enabling objects in an image to have apparent 3D motion. Many researchers have solved this issue by reconstructing 3D model from several images using image-based modeling techniques, or building a cube-modeled scene from camera calibration using vanishing points. This paper, however, presents the possibility of image-based motion without exact 3D information of scene geometry and camera calibration. The proposed system considers the image plane as a projective plane with respect to a view point and models a 2D frame of a projected 3D object using only lines and points. And a modeled frame refers to its vanishing points as local coordinates when it is transformed.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.202-205
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• 2007

본 논문에서는 3D 점집합으로부터 3D 메쉬를 생성하는 효율적인 기법을 소개한다. 대표적인 3D 삼각화 방법으로 3D Delaunay 삼각화 기법이 있으나 물체의 표면만을 고려한 메쉬 생성을 위한 방법으로 비효율적인 측면이 있다. 본 논문에서는 적은 계산량으로 물체의 표면 메쉬를 생성하는 기법을 소개한다. 물체의 각 영역을 분할하고 각 영역에 대해서 2D Delaunay 삼각화를 적용하여 3D 메쉬 구조를 얻는다. 3차원 점 집합에 대해 OBB를 계산하고 이를 기준으로 점집합을 다양한 각도에서 자르고 각 부분 점집합에 대해서 2D Delaunay 삼각화를 실시한다. 절단하는 각도의 간격이나 폭은 원래의 3D 점집합에서의 가장 가까운 이웃점들까지의 평균 거리를 이용하여 결정하도록 하였다. 후처리 과정으로 삼각 분할과정에서 잘못된 에지의 연결을 제거함으로써 객체의 삼각 분할 결과를 향상시킨다. 제안된 메쉬 생성 기법은 다양한 영상 기반 모델링 응용에서 효과적으로 적용될 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.326-331
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• 2019

In the real world, there are both dynamic objects and static objects, but an autonomous vehicle or mobile robot cannot distinguish between them, even though a human can distinguish them easily. It is important to distinguish static objects from dynamic objects clearly to perform autonomous driving successfully and stably for an autonomous vehicle or mobile robot. To do this, various sensor systems can be used, like cameras and LiDAR. Stereo camera images are used often for autonomous driving. The stereo camera images can be used in object recognition areas like object segmentation, classification, and tracking, as well as navigation areas like 3D world reconstruction. This study suggests a method to distinguish static/dynamic objects using stereo vision for an online autonomous vehicle and mobile robot. The method was applied to a 3D world map reconstructed from stereo vision for navigation and had 99.81% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.31-39
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for various applications such as intelligent robots, intelligent vehicles, intelligent buildings,..etc. First, we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds.

• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.135-145
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for intelligent robots. First. we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds. The experimental results show the feasibility of real-time and behavior-oriented 3D modeling of workspace for robotic manipulative tasks.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.429-436
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• 2010

This paper presents a method to extract 3D outlines of objects in an image obtained from a monocular vision. After detecting the general outlines of the object by MOPS(Multi-Scale Oriented Patches) -algorithm and we obtain their spatial coordinates. Simultaneously, it obtains the space-coordinates with feature points to be immanent within the outlines of objects through SIFT(Scale Invariant Feature Transform)-algorithm. It grasps a form of objects to join the space-coordinates of outlines and SIFT feature points. The method which is proposed in this paper, it forms general outlines of objects, so that it enables a rapid calculation, and also it has the advantage capable of collecting a detailed data because it supplies the internal-data of outlines through SIFT feature points.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1910-1924
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• 1994

Curved 3D objects represented by range data contain large amounts of information compared with planar objects, but do not have distinct features for matching to those of object models. This makes it difficult to represent and identify a general 3D curved object. This paper introduces a new view-point independent approach to recognizing general 3D curved objects using range data. Our approach makes use of the relative geometric differences between particular points on the object surface and some model points. The model points are prespecified arbitrarily and keeping the task in mind so that the following task can be easily described using the model points. Our approach has several advantages. Since model points are specified arbitrarily and task dependently, further processing can be reduced in application by locating the model points at places which are useful for further operations in the task. The knowledge base is simple with less storage requirement. And, it is easy to compensate the uncertainties of positions estimation caused by noise and quantization error.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.512-514
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• 1999

효율적인 물체인식을 위해서는 물체의 형상특징을 직선적으로 기술할 수 있는 체적소 기반 물체 표현 방법이 필요하다. 본 논문에서는 몇 개의 계수를 가지고 3차원 정보를 효율적으로 표현할 수 있는 superquadric을 이용하여 기본적인 3차원 물체를 모델링 한다. 그리고 보다 복잡하고 정교한 물체의 표현을 위해서 변형된 superquadric을 함께 이용한다. 이렇게 만들어진 개개의 3차원 모델에 z-buffer 알고리즘을 적용하여 하나의 완전한 3차원 물체로 표현하는 방법을 제시하고 실험을 통해 그 유용성을 입증하였다.