Connected Component Search in Image Processing with MATLAB Implementation

In this article, we will explore how to implement connected component search in image processing using MATLAB. First, let's understand the definition and purpose of connected component search. This algorithm is used to identify and label target regions in digital images, with applications in image segmentation, object detection, and tracking systems.

For this implementation, we will develop MATLAB code to demonstrate the algorithm's principles. The process begins with image reading and preprocessing stages to enhance search efficiency using functions like imread() and im2bw() for binarization. We will then detail the implementation of the connected component search algorithm, typically employing either 4-connectivity or 8-connectivity neighborhood checking using bwlabel() or custom traversal functions. The demonstration will show how to apply the algorithm to identify target regions through region property analysis with regionprops() function. Finally, we will present simulation results displaying labeled components and discuss optimization techniques such as union-find algorithms for improved performance and accuracy.

Through this tutorial, you will gain comprehensive understanding of connected component search mechanics and learn practical MATLAB implementation skills. Additionally, you'll master image preprocessing techniques for efficiency enhancement and acquire algorithm optimization strategies for better performance in real-world applications.