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Effective Depth Detection of Three-Dimensional Objects in Space with Picked-up and Computationally Reconstructed Integral Images

机译:利用拾取和计算重建的积分图像对空间中的三维物体进行有效的深度检测

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摘要

A new approach to effectively extract depth data of three-dimensional (3D) objects in space using elemental images picked-up from 3D objects and their computationally reconstructed plane object images (POIs) is proposed. For this approach, two image concepts are introduced: one is the mapped elemental image (MEI) defined as an inversely flipped and magnified version of the picked-up elemental image and the other is the reconstructed area image (RAI) defined as a part of the computationally reconstructed POI segmented with the area exactly overlapped with the MEI. Then, depth data of the 3D objects can be extracted through correlations between the MEIs and RAIs along the output plane. That is, a depth map can be constructed from depth data extracted from each of the picked-up elemental images, and by analyzing this depth map, the specific location of the 3D objects in space can be determined. To show the feasibility of the proposed method, some experiments with virtual and real 3D objects are performed and the results are discussed.
机译:提出了一种新的方法,该方法利用从3D对象中拾取的元素图像及其计算重建的平面对象图像(POI)有效提取空间中的三维(3D)对象的深度数据。对于此方法,引入了两个图像概念:一个是映射的基本图像(MEI),它被定义为所拾取基本图像的反向翻转和放大版本,另一个是定义为图像的一部分的重构区域图像(RAI)。计算重建的POI,其区域与MEI完全重叠。然后,可以通过沿着输出平面的MEI和RAI之间的相关性来提取3D对象的深度数据。即,可以从从每个拾取的基本图像中提取的深度数据来构造深度图,并且通过分析该深度图,可以确定空间中的3D对象的特定位置。为了显示该方法的可行性,对虚拟和真实3D对象进行了一些实验,并对结果进行了讨论。

著录项

  • 来源
    《Japanese journal of applied physics》 |2009年第4issue1期|169-179|共11页
  • 作者单位

    3D Display Research Center, Department of Electronic Engineering, Kwangwoon University, 447-1 Wolge-dong, Nowon-gu, Seoul 139-701, Korea;

    3D Display Research Center, Department of Electronic Engineering, Kwangwoon University, 447-1 Wolge-dong, Nowon-gu, Seoul 139-701, Korea;

    3D Display Research Center, Department of Electronic Engineering, Kwangwoon University, 447-1 Wolge-dong, Nowon-gu, Seoul 139-701, Korea;

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  • 入库时间 2022-08-18 03:16:37

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