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Fast Fourier Transform

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Fast computational depth segmentation using orthogonal fringe patterns without pattern sequence changing.

Yu Xiao, Wenzhong Han, Xuejing Zhang

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|April 2, 2021

Summary

This summary is machine-generated.

A new fast computational depth segmentation (FCDS) method uses five fringe patterns for rapid object segmentation. This approach improves speed and reduces computational costs compared to omnidirectional depth segmentation methods.

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Area of Science:

Computer Vision
Optical Metrology
Image Processing

Background:

Omnidirectional depth segmentation (ODSM) offers robustness but suffers from slow speeds and high computational costs due to multiple fringe patterns and sequence changes.
Traditional methods require numerous fringe patterns and complex sequence manipulations, limiting practical applications.

Purpose of the Study:

To propose a Fast Computational Depth Segmentation (FCDS) method that enhances segmentation speed and reduces computational complexity.
To enable efficient depth segmentation using fewer fringe patterns and without sequence alterations.

Main Methods:

Introduced a Fast Computational Depth Segmentation (FCDS) method utilizing only five fringe patterns (two orthogonal phase-shifting and one DC component).
Employed phase singularity points as markers for extracting segmenting lines crucial for depth determination.
Integrated a modified Fourier transform algorithm (MFTA) for calculating wrapped phase sequences.
Implemented an optimization algorithm to refine depth segmenting lines, addressing mis-segmentation issues.

Main Results:

The FCDS method successfully segments objects at different depths into isolated regions without changing fringe pattern sequences.
Achieved significant reduction in computational costs and improved phase insensitivity.
Demonstrated effectiveness in segmenting objects with abrupt depth changes.
Experimental results show up to a 120% increase in segmentation speed compared to previous methods, particularly for objects of similar color.

Conclusions:

The proposed FCDS method offers a faster and computationally efficient alternative for omnidirectional depth segmentation.
Successfully addresses limitations of previous methods, including speed, complexity, and mis-segmentation.
Validated through simulations and experiments, proving its precision and effectiveness for real-world applications.