Wavelet Transform-Based Foreign Object Detection Algorithm Implemented in MATLAB

This MATLAB-implemented algorithm based on wavelet transform is designed to detect foreign objects in images. The implementation primarily involves the following key steps with corresponding code approaches:

1. Image Wavelet Decomposition: The algorithm employs wavelet transform functions like wavedec2() to decompose images into multi-scale frequency bands, enabling better analysis of image features across different resolution levels. This hierarchical decomposition helps in separating noise from relevant image structures.

2. Edge Detection Using Wavelet Transform: The implementation utilizes wavelet-based edge detection methods, potentially using functions such as wthresh() for thresholding and edge() with wavelet-specific parameters. This process extracts object contours and edge information by analyzing high-frequency components in wavelet sub-bands, particularly focusing on horizontal, vertical, and diagonal details.

3. Foreign Object Detection: By combining wavelet decomposition results and edge detection outputs, the algorithm identifies and locates anomalies through techniques like coefficient analysis, statistical thresholding, and morphological operations. The implementation may involve comparing wavelet coefficients across scales to detect irregularities that indicate foreign objects or defects.

This algorithm provides an effective solution for foreign object detection with applications in image processing, computer vision, and object recognition. The MATLAB implementation typically involves Wavelet Toolbox functions for decomposition and custom logic for anomaly detection based on coefficient analysis and edge pattern recognition.