TRUCO: A Scalable Lock-Free Algorithm for Parallel Contour Extraction

Rafael Munoz-Salinas, Francisco J. Romero-Ramirez, Manuel J. Marin-Jimenez

Transforming binary masks into geometric contours remains a critical step in many computer vision pipelines, particularly for post-processing high-resolution segmentation outputs. Yet, standard algorithms such as the Suzuki-Abe method are limited by their sequential nature and the overhead of building topological hierarchies. As modern applications transition to high-resolution imaging, there is a critical need for contour extraction that leverages multi-core CPU architectures without the complexity of tile stitching or heavy synchronization. This paper presents TRUCO (Threaded Raster Unrestricted Contour Ownership), a lock-free parallel algorithm designed for high-performance geometric boundary extraction. TRUCO introduces a start-point ownership rule, combined with a speculative tracing model, in which threads trace contours downward into adjacent regions and use an upward-termination logic to ensure each contour is processed exactly once. Experimental results on synthetic and real-world datasets, conducted on both consumer and server-grade CPUs, demonstrate that TRUCO significantly outperforms existing baselines. The method achieves speedups of over an order of magnitude…