Pose detection of 3-D objects using S2-correlated images and discrete spherical harmonic transforms
Pose detection of 3-D objects using S2-correlated images and discrete spherical harmonic transforms
复制标题
使用 S2 相关图像和离散球谐变换对 3D 对象进行姿态检测
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
R. Roberts
中科院分区：
文献类型：
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作者：
R. Hoover;A. A. Maciejewski;R. Roberts
从二维图像中检测三维物体的姿态是计算机视觉和机器人应用中的一个重要问题。具体的例子包括自动化装配、自动化零件检测、机器人焊接和人机交互，以及其他许多方面。特征分解是处理这个问题的常用技术，并已被应用于相关图像集。不幸的是，对于3-D对象的姿态检测，必须从许多不同的方向捕获非常大量的相关图像。因此，这一大型图像集的特征分解在计算上非常昂贵。在这项工作中，我们提出了一种方法，用于捕获图像的对象从许多位置通过采样S2适当。使用该球面采样模式，由于S2中的相关性，可以通过使用球面谐波变换来“浓缩”信息来减少计算本征分解的计算负担。我们提出了一个计算效率高的算法近似的特征分解的基础上的球谐变换分析。实验结果进行比较和对比的算法对真正的特征分解，以及量化的计算节省。
The pose detection of three-dimensional (3-D) objects from two-dimensional (2-D) images is an important issue in computer vision and robotics applications. Specific examples include automated assembly, automated part inspection, robotic welding, and human robot interaction, as well as a host of others. Eigendecomposition is a common technique for dealing with this issue and has been applied to sets of correlated images for this purpose. Unfortunately, for the pose detection of 3-D objects, a very large number of correlated images must be captured from many different orientations. As a result, the eigendecomposition of this large set of images is very computationally expensive. In this work, we present a method for capturing images of objects from many locations by sampling S2 appropriately. Using this spherical sampling pattern, the computational burden of computing the eigendecomposition can be reduced by using the spherical harmonic transform to "condense" information due to the correlation in S2. We propose a computationally efficient algorithm for approximating the eigendecomposition based on the spherical harmonic transform analysis. Experimental results are presented to compare and contrast the algorithm against the true eigendecomposition, as well as quantify the computational savings.