@InProceedings{8081563,
  author = {V. Baltazart and P. Nicolle and L. Yang},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images},
  year = {2017},
  pages = {2016-2020},
  abstract = {The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. The results on pavement images illustrate the achieved improvements in terms of better segmentation and faster computational time.},
  keywords = {crack detection;edge detection;geotechnical engineering;graphics processing units;image segmentation;roads;structural engineering computing;MPS algorithm;automatic crack detection;grey level pavement images;MPS approach;segmentation performance;crack skeleton;MPS methodology;C language;minimal path selection;Matlab coding;GPU platform;Graphics processing units;Image segmentation;Skeleton;MATLAB;Encoding;Surface cracks;Real-time systems;road surface monitoring;crack segmentation;performance assessment;optimization;parallelization;GPU},
  doi = {10.23919/EUSIPCO.2017.8081563},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347065.pdf},
}

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Yang},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images},\n year = {2017},\n pages = {2016-2020},\n abstract = {The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the first three steps of the algorithm which achieve the segmentation of the crack skeleton. This is at first the improvement of the MPS methodology under Matlab coding, then, the C language MPS version and finally, the first attempt to parallelize MPS under the GPU platform. 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