Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets

Gazagnes, Simon; Wilkinson, Michael H. F.

doi:10.1109/tip.2021.3064223

Cited by 15 publications

(23 citation statements)

References 26 publications

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“…In principle binary partition trees could be processed this way, if you allow for the fact that the resulting simplified tree might no longer be binary. It should even be possible to extend the technique to the distributed component graph used for distributed computing of attribute filters [20,29], as each local modified component tree contains all the data necessary to compute any filtering or analysis step.…”

Section: Some Initial Resultsmentioning

confidence: 99%

Component-Tree Simplification Through Fast Alpha Cuts

Wilkinson

2022

Lecture Notes in Computer Science

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Tree-based hierarchical image representations are commonly used in connected morphological image filtering, segmentation and multiscale analysis. In the case of component trees, filtering is generally based on thresholding single attributes computed for all the nodes in the tree. Alternatively, so-called shapings are used, which rely on building a component tree of a component tree to filter the image. Neither method is practical when using vector attributes. In this case, more complicated machine learning methods are required, including clustering methods. In this paper I present a simple, fast hierarchical clustering algorithm based on cuts of α-trees to simplify and filter component trees.

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Section: Some Initial Resultsmentioning

confidence: 99%

Component-Tree Simplification Through Fast Alpha Cuts

Wilkinson

2022

Lecture Notes in Computer Science

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“…As images grew in size, the same strategies were adopted for a distributed computation of the max-tree [29], [36] with the extra burden of minimizing memory exchanges between (cluster) nodes using border max-trees. This idea is even pushed further in [30], [37] with a distributed max-tree representation based on border max-trees that avoids storing the final tree in shared-memory and enables distributed tree processing.…”

Section: Parallel and Distributed Max-tree Algorithmsmentioning

confidence: 99%

“…Many algorithms have been developed for speeding-up the max-tree computation. So far, the proposed optimization techniques can roughly come under one of these three categories: (a) algorithmic optimizations, i.e., choosing between a top-down or a bottom-up construction with adapted data structures [18], [24], [25]; (b) thread level parallelism, i.e., classical parallelism for multiprocessors with shared memory (SMP) [26], [27], [28]; (c) distributed computing, i.e., joint max-tree computation between distributed memory [29], [30]. To the best of our knowledge, this is the first time a max-tree algorithm is proposed for massively parallel architectures and fits the SIMT paradigm of GPUs.…”

Section: Introductionmentioning

confidence: 99%

Max-Tree Computation on GPUs

Carlinet

Blin

Lemaître³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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In Mathematical Morphology, the max-tree is a region-based representation that encodes the inclusion relationship of the threshold sets of an image. This tree has proved useful in numerous image processing applications. For the last decade, work has led to improving the construction time of this structure; mixing algorithmic optimizations, parallel and distributed computing. Nevertheless, there is still no algorithm that benefits from the computing power of the massively parallel architectures. In this work, we propose the first GPU algorithm to compute the max-tree. The proposed approach leads to significant speed-ups, and is up to one order of magnitude faster than the current State-of-the-Art parallel CPU algorithms. This work paves the way for a max-tree integration in image processing GPU pipelines and real-time image processing based on Mathematical Morphology. It is also a foundation for porting other image representations from Mathematical Morphology on GPUs.

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“…Huang, Chen [34] investigated a parallel mean shift algorithm based on a task-scheduling method with a message-passing interface and an OpenCL (computing language) model. Gazagnes and Wilkinson [35] presented a distributed connected component filtering and analysis method by adapting the flooding techniques to build the components trees and by implementing the merging approach to extend the computation scale. This research extended the dynamic range from 2D to 3D or higher dynamic range data sets.…”

Section: Introductionmentioning

confidence: 99%

A Strategy of Parallel Seed-Based Image Segmentation Algorithms for Handling Massive Image Tiles over the Spark Platform

Chen

Wang

et al. 2021

Remote Sensing

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The volume of remote sensing images continues to grow as image sources become more diversified and with increasing spatial and spectral resolution. The handling of such large-volume datasets, which exceed available CPU memory, in a timely and efficient manner is becoming a challenge for single machines. The distributed cluster provides an effective solution with strong calculation power. There has been an increasing number of big data technologies that have been adopted to deal with large images using mature parallel technology. However, since most commercial big data platforms are not specifically developed for the remote sensing field, two main issues exist in processing large images with big data platforms using a distributed cluster. On the one hand, the quantities and categories of official algorithms used to process remote sensing images in big data platforms are limited compared to large amounts of sequential algorithms. On the other hand, the sequential algorithms employed directly to process large images in parallel over a distributed cluster may lead to incomplete objects in the tile edges and the generation of large communication volumes at the shuffle stage. It is, therefore, necessary to explore the distributed strategy and adapt the sequential algorithms over the distributed cluster. In this research, we employed two seed-based image segmentation algorithms to construct a distributed strategy based on the Spark platform. The proposed strategy focuses on modifying the incomplete objects by processing border areas and reducing the communication volume to a reasonable size by limiting the auxiliary bands and the buffer size to a small range during the shuffle stage. We calculated the F-measure and execution time to evaluate the accuracy and execution efficiency. The statistical data reveal that both segmentation algorithms maintained high accuracy, as achieved in the reference image segmented in the sequential way. Moreover, generally the strategy took less execution time compared to significantly larger auxiliary bands and buffer sizes. The proposed strategy can modify incomplete objects, with execution time being twice as fast as the strategies that do not employ communication volume reduction in the distributed cluster.

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Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets

Cited by 15 publications

References 26 publications

Component-Tree Simplification Through Fast Alpha Cuts

Component-Tree Simplification Through Fast Alpha Cuts

Max-Tree Computation on GPUs

A Strategy of Parallel Seed-Based Image Segmentation Algorithms for Handling Massive Image Tiles over the Spark Platform

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