Line Endpoint and Junction Detection Algorithm

Detecting endpoints and junctions of lines in MATLAB often presents computational challenges, but this optimized implementation significantly accelerates the process through efficient morphological processing and neighborhood analysis. The code not only detects these critical points but also returns precise spatial coordinates for both endpoints and junctions. Furthermore, it provides crucial connectivity data by quantifying the number of lines converging at each junction point.

The underlying algorithm employs skeletonization techniques followed by convolution-based neighbor counting to distinguish between endpoints (points with single neighbor) and junctions (points with three or more neighbors). This methodology finds extensive applications in various domains including vascular network analysis in medical imaging, road network extraction from satellite imagery, and minutiae detection in fingerprint recognition systems. By analyzing line connectivity patterns, the algorithm reveals essential structural information about complex network configurations.

The implementation leverages MATLAB's optimized matrix operations and morphological functions (bwmorph, conv2) to achieve high computational efficiency. This makes it particularly suitable for processing large-scale image datasets where rapid detection of topological features is required. Automation of endpoint and junction detection through this code significantly reduces manual processing time while improving analytical accuracy, making it an invaluable tool for enhancing line-based pattern recognition capabilities.