Academic Journal
GPU-Based Parallel Euclidean Distance Transform Algorithm.
| Τίτλος: | GPU-Based Parallel Euclidean Distance Transform Algorithm. |
|---|---|
| Συγγραφείς: | Lu, Yucheng, Zhu, Xiaoying, Pang, Anlong, He, Xi |
| Πηγή: | Mathematics (2227-7390); Feb2026, Vol. 14 Issue 4, p597, 21p |
| Θεματικοί όροι: | Graphics processing units, Voronoi polygons, Euclidean distance, Parallel programming, Image processing, Computer performance, Computer science |
| Περίληψη: | Euclidean distance transform (EDT) often suffers from high computational complexity and limited processing efficiency, especially when applied to large-scale images. To address these challenges, this paper proposes a GPU-based parallel EDT algorithm. The proposed approach first partitions the input image into multiple horizontal sub-blocks. For each sub-block, a row-wise recursive computation strategy is adopted to construct its Voronoi diagram in parallel, thereby reducing computational overhead by exploiting the strong structural similarity between the Voronoi diagrams of adjacent rows. Based on the Voronoi diagrams of all sub-blocks, the Euclidean distance from each pixel to the nearest background pixel is subsequently evaluated, completing the transform. Experimental results demonstrate that the proposed algorithm achieves up to a 52× speedup over traditional CPU-based EDT methods, leading to a substantial improvement in computational performance. Nevertheless, the scalability of the method is influenced by GPU memory capacity and the chosen sub-block partitioning strategy when processing extremely large images. Moreover, the core idea of leveraging inter-row Voronoi similarity to reduce redundant computation can be naturally extended to higher-dimensional exact EDT as well as approximate EDT variants. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: GPU-Based Parallel Euclidean Distance Transform Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Yucheng%22">Lu, Yucheng</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Xiaoying%22">Zhu, Xiaoying</searchLink><br /><searchLink fieldCode="AR" term="%22Pang%2C+Anlong%22">Pang, Anlong</searchLink><br /><searchLink fieldCode="AR" term="%22He%2C+Xi%22">He, Xi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Mathematics (2227-7390); Feb2026, Vol. 14 Issue 4, p597, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Voronoi+polygons%22">Voronoi polygons</searchLink><br /><searchLink fieldCode="DE" term="%22Euclidean+distance%22">Euclidean distance</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Euclidean distance transform (EDT) often suffers from high computational complexity and limited processing efficiency, especially when applied to large-scale images. To address these challenges, this paper proposes a GPU-based parallel EDT algorithm. The proposed approach first partitions the input image into multiple horizontal sub-blocks. For each sub-block, a row-wise recursive computation strategy is adopted to construct its Voronoi diagram in parallel, thereby reducing computational overhead by exploiting the strong structural similarity between the Voronoi diagrams of adjacent rows. Based on the Voronoi diagrams of all sub-blocks, the Euclidean distance from each pixel to the nearest background pixel is subsequently evaluated, completing the transform. Experimental results demonstrate that the proposed algorithm achieves up to a 52× speedup over traditional CPU-based EDT methods, leading to a substantial improvement in computational performance. Nevertheless, the scalability of the method is influenced by GPU memory capacity and the chosen sub-block partitioning strategy when processing extremely large images. Moreover, the core idea of leveraging inter-row Voronoi similarity to reduce redundant computation can be naturally extended to higher-dimensional exact EDT as well as approximate EDT variants. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/math14040597 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 597 Subjects: – SubjectFull: Graphics processing units Type: general – SubjectFull: Voronoi polygons Type: general – SubjectFull: Euclidean distance Type: general – SubjectFull: Parallel programming Type: general – SubjectFull: Image processing Type: general – SubjectFull: Computer performance Type: general – SubjectFull: Computer science Type: general Titles: – TitleFull: GPU-Based Parallel Euclidean Distance Transform Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Yucheng – PersonEntity: Name: NameFull: Zhu, Xiaoying – PersonEntity: Name: NameFull: Pang, Anlong – PersonEntity: Name: NameFull: He, Xi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277390 Numbering: – Type: volume Value: 14 – Type: issue Value: 4 Titles: – TitleFull: Mathematics (2227-7390) Type: main |
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