Extracting Phase Congruency Features from Images for Registration

In the process of image registration, extracting phase congruency features can lead to more accurate alignment results. Phase congruency features refer to the consistency of phase information across different regions of an image, which helps determine object positions and shapes within the image. Implementation typically involves applying wavelet transforms or Fourier analysis to capture local phase information, followed by calculating phase congruency maps that highlight perceptually significant features. Image registration involves calibrating positional and scale differences between various images to achieve alignment and enable comparison. The algorithm generally follows these steps: feature detection using phase congruency, feature matching through correlation techniques, and transformation estimation using methods like RANSAC or least-squares optimization. In our experiments, we utilized multiple images captured under varying angles and lighting conditions. By extracting phase congruency features and performing image registration, we obtained more precise alignment outcomes. The implementation code includes key functions such as phasecong() for feature extraction and imregister() for alignment operations. These experimental results validate the significance of phase congruency features in image registration tasks, particularly their robustness to illumination changes and geometric transformations.