GPU Sparse Ray-Traced Segmentation
This paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8718269/ |
| _version_ | 1818323412998684672 |
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| author | Lucian Petrescu Anca Morar Florica Moldoveanu Alin Moldoveanu |
| author_facet | Lucian Petrescu Anca Morar Florica Moldoveanu Alin Moldoveanu |
| author_sort | Lucian Petrescu |
| collection | DOAJ |
| description | This paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation methods do, the proposed algorithm searches both locally and remotely, using a unique ray tracing-based search strategy, which quickly covers the segmentation search space. The presented algorithm fully exploits the parallelism of the GPUs, sparsely segmenting high-resolution images (4K) in real-time on low range laptops and other mobile devices, approximately 5× times faster than the state-of-the-art simple linear iterative clustering (SLIC). While this paper demonstrates the results with images, the algorithm is trivially modifiable to work with input sets of any dimension. In contrast to the state-of-the-art real-time GPU methods, this algorithm doesn't require additional merging steps, as pre-segmentation methods do, and it produces complete segmentation. Additionally, post-segmentation optional stages for complete labeling and region merging on the GPU are also provided, although they are not always necessary. |
| first_indexed | 2024-12-13T11:12:17Z |
| format | Article |
| id | doaj.art-8e823fc69fc64c83b29a4755fd2f2a5f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:12:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8e823fc69fc64c83b29a4755fd2f2a5f2022-12-21T23:48:43ZengIEEEIEEE Access2169-35362019-01-017685116852110.1109/ACCESS.2019.29177218718269GPU Sparse Ray-Traced SegmentationLucian Petrescu0Anca Morar1https://orcid.org/0000-0002-4773-6862Florica Moldoveanu2Alin Moldoveanu3https://orcid.org/0000-0002-1368-7249Department of Computer Science, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest, RomaniaDepartment of Computer Science, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest, RomaniaDepartment of Computer Science, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest, RomaniaDepartment of Computer Science, Faculty of Automatic Control and Computers, Politehnica University of Bucharest, Bucharest, RomaniaThis paper introduces a real-time region growing segmentation algorithm, designed for graphics processing units (GPUs), which labels only a fraction of the input elements. Instead of searching locally around each element for strong similarity, like state-of-the-art segmentation and pre-segmentation methods do, the proposed algorithm searches both locally and remotely, using a unique ray tracing-based search strategy, which quickly covers the segmentation search space. The presented algorithm fully exploits the parallelism of the GPUs, sparsely segmenting high-resolution images (4K) in real-time on low range laptops and other mobile devices, approximately 5× times faster than the state-of-the-art simple linear iterative clustering (SLIC). While this paper demonstrates the results with images, the algorithm is trivially modifiable to work with input sets of any dimension. In contrast to the state-of-the-art real-time GPU methods, this algorithm doesn't require additional merging steps, as pre-segmentation methods do, and it produces complete segmentation. Additionally, post-segmentation optional stages for complete labeling and region merging on the GPU are also provided, although they are not always necessary.https://ieeexplore.ieee.org/document/8718269/GPUimage segmentationparallel processingray tracing search strategysparse segmentation |
| spellingShingle | Lucian Petrescu Anca Morar Florica Moldoveanu Alin Moldoveanu GPU Sparse Ray-Traced Segmentation IEEE Access GPU image segmentation parallel processing ray tracing search strategy sparse segmentation |
| title | GPU Sparse Ray-Traced Segmentation |
| title_full | GPU Sparse Ray-Traced Segmentation |
| title_fullStr | GPU Sparse Ray-Traced Segmentation |
| title_full_unstemmed | GPU Sparse Ray-Traced Segmentation |
| title_short | GPU Sparse Ray-Traced Segmentation |
| title_sort | gpu sparse ray traced segmentation |
| topic | GPU image segmentation parallel processing ray tracing search strategy sparse segmentation |
| url | https://ieeexplore.ieee.org/document/8718269/ |
| work_keys_str_mv | AT lucianpetrescu gpusparseraytracedsegmentation AT ancamorar gpusparseraytracedsegmentation AT floricamoldoveanu gpusparseraytracedsegmentation AT alinmoldoveanu gpusparseraytracedsegmentation |