{"title":"An Alpha-Tree Algorithm for Massively Parallel Architectures","authors":"Edwin Carlinet;Quentin Kaci;Nicolas Blin","doi":"10.1109/TIP.2025.3586495","DOIUrl":null,"url":null,"abstract":"The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"4402-4413"},"PeriodicalIF":13.7000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11079786/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.