Parallel Algorithm for Concurrent Computation of Connected Component Tree

Matas, Petr; Dokladalova, Eva; Akil, Mohamed; Grandpierre, Thierry; Najman, Laurent; Poupa, Martin; Georgiev, V.

doi:10.1007/978-3-540-88458-3_21

Cited by 20 publications

(24 citation statements)

References 8 publications

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“…Taking into account the contents of the image, maybe in combination with the min-tree and component map, while dividing the image among the threads may reduce the communications needed between the different threads, which could lead to a faster algorithm. We can also try other ways of tiling the image, following [10].…”

Section: Discussionmentioning

confidence: 99%

Towards a Parallel Topological Watershed: First Results

Neerbos

Najman

Wilkinson

2011

Mathematical Morphology and Its Applications to Image and Signal Processing

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Section: Discussionmentioning

confidence: 99%

Towards a Parallel Topological Watershed: First Results

Neerbos

Najman

Wilkinson

2011

Mathematical Morphology and Its Applications to Image and Signal Processing

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“…Assuming s constant and H = W = √ n the complexity as a function of n and k of the map-reduce algorithm is O(A(k, n)) + O(k √ n log n). When there is as many splits as rows, s is now dependent on n. This leads to Matas et al [22] algorithm whose complexity is…”

Section: A Complexity Analysismentioning

confidence: 96%

“…Merge-based algorithms consist in computing max-trees on sub-parts of images and merging back trees to get the max-tree of the whole image [17], [21], [22]. Those algorithms are typically well-suited for parallelism since they adopt a map-reduce idiom.…”

Section: Merge-based Algorithms and Parallelismmentioning

confidence: 99%

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A Comparative Review of Component Tree Computation Algorithms

Carlinet

Géraud

2014

IEEE Trans. on Image Process.

103

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Connected operators are morphological tools that have the property of filtering images without creating new contours and without moving the contours that are preserved. Those operators are related to the max-tree and min-tree representations of images, and many algorithms have been proposed to compute those trees. However, no exhaustive comparison of these algorithms has been proposed so far, and the choice of an algorithm over another depends on many parameters. Since the need for fast algorithms is obvious for production code, we present an in-depth comparison of the existing algorithms in a unique framework, as well as variations of some of them that improve their efficiency. This comparison involves both sequential and parallel algorithms, and execution times are given with respect to the number of threads, the input image size, and the pixel value quantization. Eventually, a decision tree is given to help the user choose the most appropriate algorithm with respect to the user requirements. To favor reproducible research, an online demo allows the user to upload an image and bench the different algorithms, and the source code of every algorithms has been made available.

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“…Its construction algorithms can be classified in three classes: immersion algorithms [8,9], flooding algorithms [1,10] and mergebased algorithms [11,12]. The best algorithm depends on memory restrictions and the image quantization.…”

Section: Introductionmentioning

confidence: 99%

An array-based node-oriented max-tree representation

Souza

Rittner

Lotufo

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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This paper presents an array-based node-oriented structure for the max-tree representation, which allows direct access and flexible manipulation of its nodes, and is more suitable for OpenMP parallel processing. The proposed structure is based on two arrays called node array (NA), which stores attributes of the nodes, and node index (NI), which indicates the node that each pixel belongs to. We compare it with the pixel-oriented max-tree representation based on a parent array (parent) and an ordering array (S) that allows tree traversals. We show that our max-tree representation requires less memory when the ratio between the number of image pixels and max-tree nodes is greater than 1.6, which is often the case. It is more flexible, and can compute some attributes, such as height and dynamics, with a complexity linear on the number of max-tree nodes instead of the number of image pixels. In our experiments our structure computed the height attribute on average 11.4 faster than the parent/S representation. Also, for a single area-open filter, the sequential implementation of our structure is on average 1.14 times slower and the parallel implementation in a 4-core CPU is 1.2 times faster than the parent/S structure. For an area-open filter followed by the hmax filter, our sequential implementation is 1.34 times faster and our parallel implementation is 2.32 times faster than the parent/S structure.

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Parallel Algorithm for Concurrent Computation of Connected Component Tree

Cited by 20 publications

References 8 publications

Towards a Parallel Topological Watershed: First Results

Towards a Parallel Topological Watershed: First Results

A Comparative Review of Component Tree Computation Algorithms

An array-based node-oriented max-tree representation

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