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Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and efficient implementation of connected filters. This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees—the max- and min-tree—in shared and distributed memory environments. For the node-local computation a modified version of the flooding-based algorithm of Salembier is employed. A novel tuple-based merging scheme allows to merge the acquired partial images into a globally correct view. Using the proposed approach a speed-up of up to 44.88 using 128 processing cores on eight-bit gray-scale images could be achieved. This is more than a five-fold increase over the state-of-the-art shared-memory algorithm, while also requiring only one-thirty-second of the memory.
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... Several authors have already explored the issue of attribute computation in related contexts, such as incremental attribute computation [20] and parallel or distributed algorithms for hierarchical image analysis. In [4,7,9], the authors investigate distributed memory algorithms for computation of min and max trees to perform user-defined attribute filtering and multiscale analysis of terabytes images. In [5], the method was extended to allow a posteriori attribute computation on distributed component trees. ...
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Binary Partition Hierarchies (BPHs) and Minimum Spanning Trees are key structures in hierarchical image analysis. However, the explosion in the size of image data poses a new challenge, as the memory available in conventional workstations becomes insufficient to execute classical algorithms. To address this problem, specific algorithms have been proposed for out-of-core computation of BPHs, where a BPH is actually represented by a collection of smaller trees, called a distribution, thus reducing the memory footprint of the algorithms. In this article, we address the problem of designing efficient out-of-core algorithms for computing classical attributes in distributions of BPHs, which is a necessary step towards a complete out-of-core hierarchical analysis workflow that includes tasks such as connected filtering and the generation of other representations such as hierarchical watersheds. The proposed algorithms are based on generic operations designed to propagate information through the distribution of trees, enabling the computation of attributes such as area, volume, height, minima and number of minima.
... Recently, these tools have been improved to process Giga and Tera-Scale data sets using shared-memory [7,8] and distributed-memory techniques [9,10], or a combination of both [11,12]. These approaches typically build upon a divide-and-conquer approach where the data set is split into several tiles. ...
... In [4,6,8], the authors investigate distributed memory algorithms to compute min and max trees for terabytes images. In [5], computation of minimum spanning trees of streaming images is considered. ...
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Binary Partition Hierarchies (BPH) and minimum spanning trees are fundamental data structures involved in hierarchical analysis such as quasi-flat zones or watershed. However, classical BPH construction algorithms require to have the whole data in memory, which prevent the processing of large images that cannot fit entirely in the main memory of the computer. To cope with this problem, an algebraic framework leading to a high level calculus was introduced allowing an out-of-core computation of BPHs. This calculus relies on three operations: select, join, and insert. In this article, we introduce three efficient algorithms to perform these operations providing pseudo-code and complexity analysis.
... In [4,6,8], the authors investigate distributed memory algorithms to compute min and max trees for terabytes images. In [5], computation of minimum spanning trees of streaming images is considered. ...
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Binary Partition Hierarchies (BPH) and minimum spanning trees are fundamental data structures involved in hierarchical analysis such as quasi-flat zones or watershed. However, classical BPH construction algorithms require to have the whole data in memory, which prevent the processing of large images that cannot fit entirely in the main memory of the computer. To cope with this problem, an algebraic framework leading to a high level calculus was introduced allowing an out-of-core computation of BPHs. This calculus relies on three operations: select, join, and insert. In this article, we introduce three efficient algorithms to perform these operations providing pseudo-code and complexity analysis.
... The parallel implementation is based on the flooding non-recursive Salembier's algorithm [30], the subtree merging procedure described in [31] and the concurrent direct filter [32]. Two optimisations are introduced to obtain higher performance. ...
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