@InProceedings{7362696,
  author = {S. Balla-Arabe and C. Li and V. Brost and F. Yang},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Fuzzy selecting local region level set algorithm},
  year = {2015},
  pages = {1810-1814},
  abstract = {In this work, we introduced a novel localized region based level set model which is simultaneously effective for heterogeneous object or/and background and robust against noise. As such, we propose to minimize an energy functional based on a selective local average, i.e., when computing the local average, instead to use the intensity of all the pixels surrounding a given pixel, we first give a local Gaussian fuzzy membership to be a background or an object pixel to each of these surrounding pixels and then, we use the fuzzy weighted local average of these pixels to replace the traditional local average. With the graphics processing units' acceleration, the local lattice Boltzmann method is used to solve the proposed level set equation. The algorithm is effective in presence of intensity heterogeneity, robust against noise, fast and highly parallelizable. Experimental results demonstrate subjectively and objectively the performance of the proposed framework.},
  keywords = {fuzzy set theory;graphics processing units;image processing;lattice Boltzmann methods;fuzzy selecting local region level set algorithm;heterogeneous object;energy functional minimization;local Gaussian fuzzy membership;object pixel;graphics processing unit;local lattice Boltzmann method;Level set;Graphics processing units;Robustness;Force;Mathematical model;Active contours;Image edge detection;Level set method;image segmentation;lattice Boltzmann method;graphics processing units (GPU)},
  doi = {10.1109/EUSIPCO.2015.7362696},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104967.pdf},
}

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As such, we propose to minimize an energy functional based on a selective local average, i.e., when computing the local average, instead to use the intensity of all the pixels surrounding a given pixel, we first give a local Gaussian fuzzy membership to be a background or an object pixel to each of these surrounding pixels and then, we use the fuzzy weighted local average of these pixels to replace the traditional local average. With the graphics processing units' acceleration, the local lattice Boltzmann method is used to solve the proposed level set equation. The algorithm is effective in presence of intensity heterogeneity, robust against noise, fast and highly parallelizable. Experimental results demonstrate subjectively and objectively the performance of the proposed framework.","keywords":"fuzzy set theory;graphics processing units;image processing;lattice Boltzmann methods;fuzzy selecting local region level set algorithm;heterogeneous object;energy functional minimization;local Gaussian fuzzy membership;object pixel;graphics processing unit;local lattice Boltzmann method;Level set;Graphics processing units;Robustness;Force;Mathematical model;Active contours;Image edge detection;Level set method;image segmentation;lattice Boltzmann method;graphics processing units (GPU)","doi":"10.1109/EUSIPCO.2015.7362696","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104967.pdf","bibtex":"@InProceedings{7362696,\n author = {S. Balla-Arabe and C. Li and V. Brost and F. Yang},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Fuzzy selecting local region level set algorithm},\n year = {2015},\n pages = {1810-1814},\n abstract = {In this work, we introduced a novel localized region based level set model which is simultaneously effective for heterogeneous object or/and background and robust against noise. As such, we propose to minimize an energy functional based on a selective local average, i.e., when computing the local average, instead to use the intensity of all the pixels surrounding a given pixel, we first give a local Gaussian fuzzy membership to be a background or an object pixel to each of these surrounding pixels and then, we use the fuzzy weighted local average of these pixels to replace the traditional local average. With the graphics processing units' acceleration, the local lattice Boltzmann method is used to solve the proposed level set equation. 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