Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm
| Autor: | Seng Cheong Loke, Burkhard C. Wünsche, Bruce A. MacDonald, Matthew Parsons |
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| Rok vydání: | 2021 |
| Předmět: |
DBSCAN
CPU cache Computer science business.industry Process (computing) 020207 software engineering Pattern recognition 02 engineering and technology Image segmentation Computer graphics Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Noise (video) Artificial intelligence business Information Systems |
| Zdroj: | Journal of Real-Time Image Processing. 18:2361-2376 |
| ISSN: | 1861-8219 1861-8200 |
| Popis: | Segmentation of an image into superpixel clusters is a necessary part of many imaging pathways. In this article, we describe a new routine for superpixel image segmentation (F-DBSCAN) based on the DBSCAN algorithm that is six times faster than previous existing methods, while being competitive in terms of segmentation quality and resistance to noise. The gains in speed are achieved through efficient parallelization of the cluster search process by limiting the size of each cluster thus enabling the processes to operate in parallel without duplicating search areas. Calculations are performed in large consolidated memory buffers which eliminate fragmentation and maximize memory cache hits thus improving performance. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. |
| Databáze: | OpenAIRE |
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