Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry

By Lei Huang, Qian Kemao, Bing Pan, Anand K. Asundi. 2010

319 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/1bfbc8120b8acb1f17f56cb1da105fddbed43d24

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Song Zhang
2. Sam Van der Jeught
3. Joris J. J. Dirckx
4. Chao Zuo
5. Qian Chen
6. Lei Huang
7. Minliang Zhang
8. Anand K. Asundi

In Electrical and Electronic Engineering

1. Song Zhang
2. Chao Zuo
3. Qian Chen
4. Lei Huang
5. Anand K. Asundi

In Mechanical Engineering

1. Song Zhang
2. Chao Zuo
3. Qian Chen
4. Lei Huang
5. Anand K. Asundi
6. Minliang Zhang
7. Sam Van der Jeught
8. Joris J. J. Dirckx

Citations in the corpus

• Real-time structured light profilometry: a review (2016, background): “In comparison to the Fourier transform, wavelet transforms are computationally more 221 expensive, but can better detect the local characteristics of the fringe pattern which results in 222 better measurement precision [40].”
• Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review (2016, methodology): “Many fringe analysis techniques have been proposed to extract the phase distribution from the distorted fringe(s), such as phase-shifting profilometry [12–15], Fouriertransform profilometry [5,7,16,17], windowed Fourier-transform profilometry [64,65] and wavelet-transform profilometry [66,67].”
• The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis (2016, unknown): “A comparison between FT, WFT and wavelet transform is also proposed in [126] in the case of profilometry.”
• Fringe pattern analysis using deep learning (2018, unknown): “…on a pixel-by-pixel basis, the phase estimation at a pixel in spatial methods is influenced by its neighborhoods or even all pixels in the fringe pattern, which provides better tolerance to noise at the expense of poor performance around discontinuities and isolated regions in the phase map 7,8 .”
• High-speed 3D shape measurement with structured light methods: A review (2018, methodology): “To alleviate the problems associated with TP methods, Kemao [68,69] proposed Windowed Fourier Transform WFT) method they have substantially increased the robustness [70] and roadly extended the applications [71] of FTP methods.”