Motion Estimation Implementation Using Phase Correlation Method

In computer vision, phase correlation-based motion estimation represents a widely-used technique. This method utilizes brightness and phase information within images to determine motion between two image frames. The implementation typically involves using MATLAB code, which is a high-level technical computing language specifically designed for numerical computation, data analysis, and visualization. Through MATLAB implementation, researchers can better understand the phase correlation motion estimation methodology and conduct more in-depth research and analysis. The core algorithm operates by performing Fourier transforms on both images and computing their cross-power spectrum. Key MATLAB functions involved include fft2() for 2D Fourier transforms, ifft2() for inverse transforms, and complex number operations to extract phase information. The implementation typically calculates the peak location in the phase correlation matrix to determine translational motion parameters between images. This approach offers sub-pixel accuracy and demonstrates robustness to illumination changes, making it particularly suitable for image registration applications. The code structure generally follows these steps: image preprocessing, Fourier domain transformation, phase correlation computation, peak detection, and motion vector extraction. Researchers can modify parameters like windowing functions and interpolation methods to optimize performance for specific applications such as video stabilization or medical image analysis.