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Fast differential box-counting algorithm on GPU

  • Juan Ruiz de MirasEmail author
Article
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Abstract

The differential box-counting (DBC) algorithm is the most widely used method for calculating the fractal dimension (FD) of grayscale images. FD analysis of grayscale images has important applications in fields such as shape classification, texture analysis and image segmentation. Nowadays these kinds of images can reach a very large size, especially in 3D, and there is a trend of increasing dataset size; this means that a current dataset of this type of image may need high computation times for computing the DBC. In this paper, we present an efficient implementation on graphics processing unit (GPU) of the original DBC algorithm in its optimized version for parallel processing. Our implementation with NVIDIA Compute Unified Device Architecture (CUDA) computes the DBC with high branch efficiency and very low GPU serialization. This fact allows us to obtain a very efficient GPU implementation of the algorithm. We tested our implementation on two different hardware/software platforms for a set of 2D and 3D grayscale images of increasing size. The results showed that this GPU implementation scaled very well and achieved a speedup of up to 52× with respect to a CPU single-thread implementation of the same algorithm. Against an OpenACC multi-thread implementation in CPU, our CUDA algorithm obtains a speedup of up to 6×.

Keywords

Differential box-counting Fractal dimension CUDA GPU Grayscale image 

Notes

Compliance with ethical standards

Conflict of interest

The author declares no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of JaénJaénSpain
  2. 2.Departamento de InformáticaUniversity of JaénJaénSpain

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