MATLAB Implementation of Template Matching Using SSD Method

This source code implements template matching using the Sum of Squared Differences (SSD) method in MATLAB. Template matching is a fundamental technique in computer vision for locating regions in a target image that best resemble a reference template image. The SSD algorithm works by calculating the sum of squared pixel value differences between the template and each overlapping window in the target image, with lower SSD values indicating higher similarity. The MATLAB implementation utilizes key image processing functions such as imread() for image loading, im2double() for pixel value normalization, and nested loops for efficient sliding window operations. The core computation involves: 1. Extracting image patches using matrix indexing operations 2. Calculating squared differences with element-wise operations (.^2) 3. Summing differences across color channels (if applicable) 4. Implementing boundary handling to manage image edges MATLAB's vectorized operations and built-in image processing toolbox make this implementation computationally efficient for educational and research purposes. This code serves as both a practical tool for image analysis and a learning resource for understanding fundamental computer vision algorithms, thereby contributing to advancements in visual pattern recognition research. The implementation includes error checking for image dimensions, automatic template scaling options, and visualization features to display matching results using MATLAB's plotting capabilities.