发明授权
US07599512B2 Multi-parameter highly-accurate simultaneous estimation method in image sub-pixel matching and multi-parameter highly-accurate simultaneous estimation program 失效
多参数高精度同步估计方法在图像子像素匹配和多参数高精度同步估计程序中的应用

  • 专利标题: Multi-parameter highly-accurate simultaneous estimation method in image sub-pixel matching and multi-parameter highly-accurate simultaneous estimation program
  • 专利标题(中): 多参数高精度同步估计方法在图像子像素匹配和多参数高精度同步估计程序中的应用
  • 申请号: US10542329
    申请日: 2003-10-29
  • 公开(公告)号: US07599512B2
    公开(公告)日: 2009-10-06
  • 发明人: Masao ShimizuMasatoshi Okutomi
  • 申请人: Masao ShimizuMasatoshi Okutomi
  • 申请人地址: JP Tokyo
  • 专利权人: Tokyo Institute of Technology
  • 当前专利权人: Tokyo Institute of Technology
  • 当前专利权人地址: JP Tokyo
  • 代理机构: Ladas & Parry LLP
  • 优先权: JP2003-005557 20030114
  • 国际申请: PCT/JP03/13874 WO 20031029
  • 国际公布: WO2004/063991 WO 20040729
  • 主分类号: G06K9/00
  • IPC分类号: G06K9/00
Multi-parameter highly-accurate simultaneous estimation method in image sub-pixel matching and multi-parameter highly-accurate simultaneous estimation program
摘要:
In consideration of N-dimensional similarity, a multiparameter high precision concurrent estimation method and a multiparameter high precision concurrent estimation program in image subpixel matching, which can estimate a correspondence parameter between images precisely, concurrently, and stably with a small amount of computation at high speed.The method comprises the steps of: determining a sub-sampling position where the N-dimensional similarity value between images obtained at discrete positions is maximum or minimum on a line in parallel with a certain parameter axis, and determining an N-dimensional hyperplane that most approximates the determined sub-sampling position; determining N of the N-dimensional hyperplanes with respect to each parameter axis; determining an intersection point of N of the N-dimensional hyperplanes; and setting the intersection point as a sub-sampling grid estimation position for the correspondence parameter between images that gives a maximum value or a minimum value of N-dimensional similarity in the N-dimensional similarity space.
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