A Compact Code Implementation for Connected Component Labeling Using Run-Length Algorithm

The run-length algorithm serves as an efficient method for connected component labeling, particularly well-suited for segmenting regions in binary images. This technique performs a row-by-row scan of image pixels, leveraging continuity between adjacent rows to identify and label connected regions. Compared to traditional seed-filling approaches, it significantly reduces computational complexity. The algorithm implementation typically involves three core phases: First, identifying pixel segments within each row and assigning temporary labels. Second, resolving equivalence relationships between labels across adjacent rows. Third, performing a second scan to merge all equivalent labels into final components. During processing, a union-find data structure is employed to efficiently manage label equivalence relationships, ensuring nearly linear time complexity. For developers, the run-length algorithm's advantages include low memory footprint and easy parallelization. Key implementation considerations involve handling "label conflict" edge cases - for instance, when adjacent pixel segments above belong to different labels, these must be recorded as equivalent relationships. The algorithm finds widespread applications in document analysis, medical image processing, and industrial inspection systems. In code implementation, developers typically create a run-length encoding table storing segment start/end positions and temporary labels. The union-find structure then resolves conflicts when segments from different rows connect. Finally, a relabeling pass assigns definitive labels based on the resolved equivalence classes.