Image Matching of Two Images Using MATLAB

This MATLAB program performs image matching on two images using two different matching algorithms. The first algorithm is grayscale-based template matching, which compares pixel intensity values to identify similar regions. This method typically involves sliding a template image over the target image and computing similarity metrics like normalized cross-correlation or sum of squared differences. The second algorithm is an improved version of template matching that incorporates optimizations such as multi-scale analysis or feature-based enhancements, implemented through a user-friendly graphical interface for input and output operations. Image matching is a widely used technique in computer vision that involves comparing and analyzing images to identify similar sections or objects. This technology has significant applications across various domains including facial recognition, object tracking, and medical image analysis. The grayscale-based template matching algorithm works by calculating similarity measures between the template and target image regions, with MATLAB functions like normxcorr2() commonly used for normalized cross-correlation implementation. The improved template matching algorithm builds upon the basic approach by incorporating techniques such as pyramid matching or feature extraction to enhance accuracy and computational efficiency. Both algorithms are accessible through a MATLAB GUI interface that allows users to select input images, choose matching parameters, and visualize results. The grayscale matching implementation typically involves converting images to grayscale, defining regions of interest, and applying correlation-based matching functions. The enhanced algorithm may include preprocessing steps like edge detection or feature point extraction using methods like SURF or Harris corner detection before performing matching operations. Through these algorithmic implementations, the program achieves accurate image matching and identifies highly similar image regions or objects with improved performance characteristics.