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A frequency domain performance analysis of Horn and Schunck's optical flow algorithm for deformable motion
1Dept. of Electr. Eng., Auburn Univ., AL.
Summary
This study analyzes the Horn and Schunck optical flow (HSOF) algorithm for deformable motion estimation. Simulations show HSOF outperforms Anandan
Area of Science:
- Computer Vision
- Image Processing
- Motion Estimation
Background:
- The Horn and Schunck optical flow (HSOF) algorithm is a foundational method for estimating motion from image sequences.
- Accurate estimation of deformable motion is crucial in various fields, including robotics and medical imaging.
- Understanding the performance limitations and noise characteristics of optical flow algorithms is essential for practical applications.
Purpose of the Study:
- To present a frequency domain performance analysis of the Horn and Schunck optical flow (HSOF) algorithm.
- To model noise sources within the HSOF algorithm using the discrete Fourier transform.
- To derive an expression for the expected performance of the optical flow estimate for arbitrary discrete brightness patterns.
Main Methods:
- Frequency domain analysis of the HSOF algorithm.
- Modeling of noise sources using the discrete Fourier transform of brightness patterns.
- Derivation of an expected performance expression using noise models and prior estimation error covariance functions.
Main Results:
- A novel noise model for the HSOF algorithm is developed.
- An expression for the expected performance of the optical flow estimate is derived.
- Simulation results validate the proposed methods and demonstrate HSOF's superior accuracy over Anandan's method for specific low-frequency patterns.
Conclusions:
- The frequency domain analysis provides a robust framework for understanding HSOF algorithm performance.
- The derived performance expression is applicable to arbitrary discrete brightness patterns.
- HSOF demonstrates improved accuracy for certain low-frequency deformable motion patterns compared to Anandan's optical flow estimate.
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