@InProceedings{8553321,
  author = {S. Konlambigue and J. Pothin and P. Honeine and A. Bensrhair},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Fast and Accurate Gaussian Pyramid Construction by Extended Box Filtering},
  year = {2018},
  pages = {400-404},
  abstract = {Gaussian Pyramid (GP) is one of the most important representations in computer vision. However, the computation of G P is still challenging for real-time applications. In this paper, we propose a novel approach by investigating the extended box filters for an efficient Gaussian approximation. Taking advantages of the cascade configuration, tiny kernels and memory cache, we develop a fast and suitable algorithm for embedded systems, typically smartphones. Experiments with Android NDK show a 5× speed up compared to an optimized CPU-version of the Gaussian smoothing.},
  keywords = {computer vision;embedded systems;Gaussian processes;image processing;operating system kernels;optimisation;smart phones;Gaussian Pyramid;GP;important representations;computer vision;real-time applications;extended box filters;efficient Gaussian approximation;cascade configuration;memory cache;suitable algorithm;Gaussian smoothing;Android NDK;Convolution;Kernel;Two dimensional displays;Signal processing algorithms;Europe;Computer vision;Gaussian pyramid;extended box filters;computer vision;SIFT},
  doi = {10.23919/EUSIPCO.2018.8553321},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437148.pdf},
}

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However, the computation of G P is still challenging for real-time applications. In this paper, we propose a novel approach by investigating the extended box filters for an efficient Gaussian approximation. Taking advantages of the cascade configuration, tiny kernels and memory cache, we develop a fast and suitable algorithm for embedded systems, typically smartphones. 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