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Single-shot surface profiling by local model fitting.

Masashi Sugiyama1, Hidemitsu Ogawa, Katsuichi Kitagawa

  • 1Department of Computer Science, Tokyo Institute of Technology, Japan. sugi@cs.titech.ac.jp

Applied Optics
|October 28, 2006
PubMed
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A novel local model fitting (LMF) algorithm offers fast and robust surface profiling using a single image. This method accurately recovers sharp edges and measures objects with varied materials, outperforming traditional techniques.

Area of Science:

  • Computer Vision
  • Image Processing
  • Metrology

Background:

  • Traditional surface profiling methods often rely on assumptions of surface smoothness, limiting their ability to reconstruct sharp features.
  • Existing techniques can be sensitive to environmental factors like vibration, impacting measurement accuracy and speed.
  • Measuring objects with heterogeneous materials presents challenges for conventional surface reconstruction algorithms.

Purpose of the Study:

  • To introduce a new surface profiling algorithm, the local model fitting (LMF) method.
  • To develop a fast and robust single-shot surface profiling technique.
  • To enable the accurate recovery of sharp edges and measurement of surfaces with heterogeneous materials.

Main Methods:

  • The local model fitting (LMF) algorithm utilizes a single image for surface profiling (single-shot).

Related Experiment Videos

  • LMF assumes local constancy of the target surface instead of band-limited smoothness.
  • The algorithm processes only local image data, enhancing its applicability to complex surfaces.

    • Main Results:

    • The LMF method demonstrates robustness against vibration due to its single-shot nature.
    • Accurate recovery of sharp edges on the target surface was achieved.
    • Experimental results confirm the effectiveness of the LMF method for measuring objects with heterogeneous materials.

    Conclusions:

    • The proposed local model fitting (LMF) method is a simple, computationally efficient, and effective algorithm for surface profiling.
    • LMF overcomes limitations of traditional methods by not requiring smoothness assumptions and enabling sharp edge recovery.
    • The algorithm's ability to use local image data makes it suitable for a wider range of materials and surface types.