• Title/Summary/Keyword: 3D Object

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A Research on the Measurement of Human Factor Algorithm 3D Object (3차원 영상 객체 휴먼팩터 알고리즘 측정에 관한 연구)

  • Choi, Byungkwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.14 no.2
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    • pp.35-47
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    • 2018
  • The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

A Sketch-based 3D Object Retrieval Approach for Augmented Reality Models Using Deep Learning

  • Ji, Myunggeun;Chun, Junchul
    • Journal of Internet Computing and Services
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    • v.21 no.1
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    • pp.33-43
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    • 2020
  • Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

A Derivation of the Accuracy Relationship between the Reconstruction of 3D Object Coordinates and the Number of Closed Curves (폐곡선의 수에 따른 3차원 물체의 좌표 복원 정확도 관계 도출)

  • Lee, Deokwoo
    • Journal of Korea Multimedia Society
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    • v.20 no.7
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    • pp.1004-1013
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    • 2017
  • This paper presents a relationship between the number of curves and geometric parameters of a 3D object. Once the relationship is established, the number of closed curves that can reliably represent 3D object is derived. Inspired by Shannon-Nyquist Sampling Theorem, in this paper, approach for sampling rate (defined as the minimum number of curves) for 3D reconstruction is proposed. The relationship is straightforward, is suitable for application to 3D object overlaid with closed-continuous curves, and can achieve efficient 3D reconstruction system in practice. To substantiate the proposed approach, simulation results are provided and the results show that the number of curves can be decreased without loss of generality of characteristics of a target 3D object.

The Extraction Vertex on 3-D Object using 3-D Curvature (3차원 곡률을 이용한 3차원물체의 정점 추출)

  • Yun, Hyeong-Tae
    • The Transactions of the Korea Information Processing Society
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    • v.3 no.6
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    • pp.1616-1623
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    • 1996
  • In general, in order to recognize and modelling the 3-D object, it is necessary to have the method to express the shape of 3-D object. In case of 2-D like silhouette image, the extraction of vertex on the boundary of the object can be obtained by using the 2-D curvature function. But, in case of 3-D curvature function that can calculate the surface curvature values of 3-d object doesn't exist, it is difficult to express the share of 3-D object. Therefore, in this paper, a new method is presented. With this presented method, the approximated surface curvature values and vertex of 3-D object can be obtained effectively using the principle of 2-D curvature and the least square method.

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3D Object Modeling and Feature Points using Octree Model (8진트리 모델을 사용한 3D 물체 모델링과 특징점)

  • 이영재
    • Journal of Korea Multimedia Society
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    • v.5 no.5
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    • pp.599-607
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    • 2002
  • The octree model, a hierarchical volume description of 3D objects, nay be utilized to generate projected images from arbitrary viewing directions, thereby providing an efficient means of the data base for 3D object recognition and other applications. We present 2D projected image and made pseudo gray image of object using octree model and multi level boundary search algorithm. We present algorithm for finding feature points of 2D and 3D image and finding matched points using geometric transformation. The algorithm is made of data base, it will be widely applied to 3D object modeling and efficient feature points application for basic 3D object research.

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Moving Object Segmentation-based Approach for Improving Car Heading Angle Estimation (Moving Object Segmentation을 활용한 자동차 이동 방향 추정 성능 개선)

  • Chiyun Noh;Sangwoo Jung;Yujin Kim;Kyongsu Yi;Ayoung Kim
    • The Journal of Korea Robotics Society
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    • v.19 no.1
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    • pp.130-138
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    • 2024
  • High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.

Object Recognition of Robot Using 3D RFID System

  • Roh, Se-Gon;Park, Jin-Ho;Lee, Young-Hoon;Choi, Hyouk-Ryeol
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.62-67
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    • 2005
  • Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) technology has been suggested to support recognition and has been rapidly and widely applied. This paper introduces the more advanced RFID-based recognition. A novel tag named 3D tag, which facilitates the understanding of the object, was designed. The previous RFID-based system only detects the existence of the object, and therefore, the system should find the object and had to carry out a complex process such as pattern match to identify the object. 3D tag, however, not only detects the existence of the object as well as other tags, but also estimates the orientation and position of the object. These characteristics of 3D tag allows the robot to considerably reduce its dependence on other sensors required for object recognition the object. In this paper, we analyze the 3D tag's detection characteristic and the position and orientation estimation algorithm of the 3D tag-based RFID system.

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A Survey for 3D Object Detection Algorithms from Images

  • Lee, Han-Lim;Kim, Ye-ji;Kim, Byung-Gyu
    • Journal of Multimedia Information System
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    • v.9 no.3
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    • pp.183-190
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    • 2022
  • Image-based 3D object detection is one of the important and difficult problems in autonomous driving and robotics, and aims to find and represent the location, dimension and orientation of the object of interest. It generates three dimensional (3D) bounding boxes with only 2D images obtained from cameras, so there is no need for devices that provide accurate depth information such as LiDAR or Radar. Image-based methods can be divided into three main categories: monocular, stereo, and multi-view 3D object detection. In this paper, we investigate the recent state-of-the-art models of the above three categories. In the multi-view 3D object detection, which appeared together with the release of the new benchmark datasets, NuScenes and Waymo, we discuss the differences from the existing monocular and stereo methods. Also, we analyze their performance and discuss the advantages and disadvantages of them. Finally, we conclude the remaining challenges and a future direction in this field.

Development of the Stereo Camera System for Active Remote Monitoring (능동적 원격감시를 위한 스테레오 카메라 시스템의 개발)

  • Park, K.;Cho, D. H.
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.10a
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    • pp.437-441
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    • 1997
  • In the conventional remote monitoring system, a user in front of a computer monitor can acquire only 2 dimensional visual information in a passive way. Thus, even thoght the user finds an interesting object from the video image, helshe can hardly acquire additional information on the object such as name. 311 shape, etc. In this paper, an active monitoring system that shows additional information on the selected object is proposed. The active remote monitoring system can calculate the 3D position of the object that is selected in the video images. Then, using the 3D position of the object, other information on the object can be retrieved from the database and shown on the screen. To calculate the 3D position of the object, 2 CCD cameras that can be tilted and panned using 3 stepping motors are used. The algorithm of 3D position calculation and the result of experiments are explained.

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A Review of 3D Object Tracking Methods Using Deep Learning (딥러닝 기술을 이용한 3차원 객체 추적 기술 리뷰)

  • Park, Hanhoon
    • Journal of the Institute of Convergence Signal Processing
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    • v.22 no.1
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    • pp.30-37
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    • 2021
  • Accurate 3D object tracking with camera images is a key enabling technology for augmented reality applications. Motivated by the impressive success of convolutional neural networks (CNNs) in computer vision tasks such as image classification, object detection, image segmentation, recent studies for 3D object tracking have focused on leveraging deep learning. In this paper, we review deep learning approaches for 3D object tracking. We describe key methods in this field and discuss potential future research directions.