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arXiv:2610.37297 · v1

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

Rafael Munoz-SalinasFrancisco J. Romero-RamirezManuel J. Marin-Jimenez
cs.CV
首次提交 2026-10-27 22:18
版本更新 2026-10-27 22:18
雷达观测 2026-10-29 00:34
arXiv ↗PDF ↗

摘要

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 compared to the industry standard while maintaining robust scalability on high-resolution scenes across diverse hardware architectures.

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v2 · 2026-11-01 22:05 · 最新观测版本

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

Extracting closed pixel contours from a binary image, together with the hierarchy that records which contours enclose which, is a basic step in many computer vision pipelines, from fiducial marker detection to the vectorization of segmentation masks. The reference algorithm, by Suzuki and Abe, is inherently sequential: each contour is found by a raster scan that depends on the labels left by all previous contours, and its parent is obtained from the last contour met by the scan. This paper presents TRUCO (Threaded Raster Unrestricted Contour Ownership), a multi-core algorithm that returns exactly the output of Suzuki--Abe (the same contours, in the same order, with the same hierarchy) without locks, read-modify-write operations or a stitching phase. The method rests on two ideas. First, the image is divided into horizontal stripes, one per thread, and two rules, row-local writes and geometric contour ownership, let each thread trace complete contours independently of the others. Second, the tracing of the contours is separated from the computation of the hierarchy, which is recovered afterwards from one reference per contour; the trace therefore stores no contour numbers in the label image, which also makes it more efficient than that of the original algorithm when a single thread is used. We prove that the output is deterministic and identical to that of Suzuki--Abe, and that the algorithm is free of data races. Experiments on synthetic images, high-resolution photographs and segmentation masks, on a desktop and a server CPU, show that TRUCO scales with the number of threads and is several times faster than the fastest sequential methods, with and without the hierarchy. With a single thread, it is already faster than Suzuki--Abe.

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v1 · 2026-10-27 22:18

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

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 compared to the industry standard while maintaining robust scalability on high-resolution scenes across diverse hardware architectures.

查看此版本解读 →查看此版本原文 ↗