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Two dimensional shape recognition using complex Fourier analysis and extension to three dimensional shape recognition.

机译：使用复杂傅立叶分析的二维形状识别，并扩展到三维形状识别。

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摘要

Two-dimensional shape recognition has become a prevalent research area in machine vision as the amount of data in the world increases each year. Shape recognition is used in such diverse areas as robotics, manufacturing, quality control, psychology, biology, and medicine as a tool for both feature detection and image classification. While there are numerous methodologies presented in the literature for shape recognition, very few have the capability to determine the scale and orientation of a matched shape to its library template while maintaining a sparse library and robustness to noise. This thesis presents a two dimensional shape recognition method using sparse complex Fourier feature sets that are robust to scale, orientation, and noise. A method for determining the scale and orientation of the matched object using the proposed feature sets is also discussed. Finally, the method is extended to the application of three dimensional shape recognition through the use of two-dimensional slicing and principal component analysis.;The main contibution of this thesis to current shape recognition algorithms which use the Fourier transform for their feature basis is the exploitation of the phase information for rotation, magnitude information for scale, and sparsity measurements that can be used to reduce the feature set size.
机译：随着世界上数据量的逐年增加，二维形状识别已成为机器视觉研究的主要领域。形状识别已在机器人技术，制造，质量控制，心理学，生物学和医学等各种领域中用作特征检测和图像分类的工具。尽管文献中提供了许多用于形状识别的方法，但很少能确定匹配形状与其库模板的比例和方向，同时保持稀疏库和对噪声的鲁棒性。本文提出了一种使用稀疏的复杂傅立叶特征集的二维形状识别方法，该特征集对缩放，方向和噪声具有鲁棒性。还讨论了一种使用提出的特征集确定匹配对象的比例和方向的方法。最后，通过二维切分和主成分分析将该方法扩展到三维形状识别的应用。本论文对以傅立叶变换为特征基础的当前形状识别算法的主要意义是：利用旋转的相位信息，缩放的幅度信息和稀疏度测量（可用于减少特征集大小）。

著录项

  • 作者

    Hewitt, Donna E.;

  • 作者单位

    The University of Texas at San Antonio.;

  • 授予单位 The University of Texas at San Antonio.;
  • 学科 Applied Mathematics.;Engineering Electronics and Electrical.
  • 学位 M.S.
  • 年度 2011
  • 页码 128 p.
  • 总页数 128
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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