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Rigid body constrained noisy point pattern matching.

S D Morgera1, P C Cheong

  • 1Dept. of Electr. Eng., McGill Univ., Montreal, Que.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study presents a hybrid approach for noisy pattern matching, significantly reducing computational complexity. The new method offers a more efficient solution for complex pattern recognition tasks in various scientific fields.

Area of Science:

  • Computational vision
  • Robotics
  • Astronomy
  • Genetics
  • High-energy physics

Background:

  • Noisy pattern matching is a complex combinatorial optimization problem.
  • Existing methods often result in suboptimal solutions due to high computational complexity.
  • Applications span diverse fields like computer vision, robotics, and genetics.

Purpose of the Study:

  • To develop an efficient hybrid approach for noisy pattern matching.
  • To address the computational challenges of least-squares pattern matching under rigid motion constraints.
  • To reduce the complexity of pattern matching algorithms.

Main Methods:

  • A hybrid approach combining steepest-ascent and singular value decomposition (SVD) was developed.

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  • The method is designed for unordered sets under Euclidean space E(n).
  • It specifically targets pattern matching problems with rigid motion constraints.

    • Main Results:

    • The hybrid approach reduces computational complexity from p(p-1)...(p-q+1) to l(21).n(4)+l(12).p(3).
    • This significant reduction makes previously intractable problems solvable.
    • The efficiency gains are crucial for large datasets (p and q >= 10^3).

    Conclusions:

    • The developed hybrid method offers a computationally efficient solution for noisy pattern matching.
    • It effectively handles rigid motion constraints, improving accuracy and speed.
    • This advancement has broad implications for various scientific and engineering disciplines.