Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 2.01 MB
Add to list
  1. Search results
  2. Separability criteria for the evaluat...

Separability criteria for the evaluation of boundary detection benchmarks

Carlos Lopez-Molina, Humberto Bustince and Bernard De Baets (UGent)
(2016) IEEE TRANSACTIONS ON IMAGE PROCESSING. 25(3). p.1047-1055
Author
Organization
Abstract
There exist a significant number of benchmarks for evaluating the performance of boundary detection algorithms, most of them relying on some sort of comparison of the automatically-generated boundaries with human-labeled ones. Such benchmarks are composed of a representative image data set, as well as a comparison measure on the universe of boundary images. Despite many such data sets and measures have been proposed, there is no clear way of knowing which combinations of them are the most suitable for the task. In this paper, we introduce four criteria that allow for a sensible evaluation of the performance of a comparison measure on a given data set. The criteria mimic the way in which humans understand boundary images, as well as their ability to recognize the underlying scenes. These criteria can, as a final goal, quantify the ability of the boundary detection benchmarks to evaluate the performance of boundary detection methods, either edge-based or segmentation-based.
Keywords
Boundary comparison, boundary image evaluation, edge detection, segmentation, error measure, IMAGE SEGMENTATION ALGORITHMS, EDGE-DETECTION, MATHEMATICAL MORPHOLOGY, PERFORMANCE EVALUATION, HAUSDORFF DISTANCE, ASSIGNMENT PROBLEM, SIMILARITY, COLOR

Downloads

  • Download
    (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 2.01 MB

Citation

Please use this url to cite or link to this publication:

MLA
Lopez-Molina, Carlos, et al. “Separability Criteria for the Evaluation of Boundary Detection Benchmarks.” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 25, no. 3, 2016, pp. 1047–55, doi:10.1109/TIP.2015.2510284.
APA
Lopez-Molina, C., Bustince, H., & De Baets, B. (2016). Separability criteria for the evaluation of boundary detection benchmarks. IEEE TRANSACTIONS ON IMAGE PROCESSING, 25(3), 1047–1055. https://doi.org/10.1109/TIP.2015.2510284
Chicago author-date
Lopez-Molina, Carlos, Humberto Bustince, and Bernard De Baets. 2016. “Separability Criteria for the Evaluation of Boundary Detection Benchmarks.” IEEE TRANSACTIONS ON IMAGE PROCESSING 25 (3): 1047–55. https://doi.org/10.1109/TIP.2015.2510284.
Chicago author-date (all authors)
Lopez-Molina, Carlos, Humberto Bustince, and Bernard De Baets. 2016. “Separability Criteria for the Evaluation of Boundary Detection Benchmarks.” IEEE TRANSACTIONS ON IMAGE PROCESSING 25 (3): 1047–1055. doi:10.1109/TIP.2015.2510284.
Vancouver
1.
Lopez-Molina C, Bustince H, De Baets B. Separability criteria for the evaluation of boundary detection benchmarks. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2016;25(3):1047–55.
IEEE
[1]
C. Lopez-Molina, H. Bustince, and B. De Baets, “Separability criteria for the evaluation of boundary detection benchmarks,” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 25, no. 3, pp. 1047–1055, 2016.
@article{8069448,
  abstract     = {{There exist a significant number of benchmarks for evaluating the performance of boundary detection algorithms, most of them relying on some sort of comparison of the automatically-generated boundaries with human-labeled ones. Such benchmarks are composed of a representative image data set, as well as a comparison measure on the universe of boundary images. Despite many such data sets and measures have been proposed, there is no clear way of knowing which combinations of them are the most suitable for the task. In this paper, we introduce four criteria that allow for a sensible evaluation of the performance of a comparison measure on a given data set. The criteria mimic the way in which humans understand boundary images, as well as their ability to recognize the underlying scenes. These criteria can, as a final goal, quantify the ability of the boundary detection benchmarks to evaluate the performance of boundary detection methods, either edge-based or segmentation-based.}},
  author       = {{Lopez-Molina, Carlos and Bustince, Humberto and De Baets, Bernard}},
  issn         = {{1057-7149}},
  journal      = {{IEEE TRANSACTIONS ON IMAGE PROCESSING}},
  keywords     = {{Boundary comparison,boundary image evaluation,edge detection,segmentation,error measure,IMAGE SEGMENTATION ALGORITHMS,EDGE-DETECTION,MATHEMATICAL MORPHOLOGY,PERFORMANCE EVALUATION,HAUSDORFF DISTANCE,ASSIGNMENT PROBLEM,SIMILARITY,COLOR}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{1047--1055}},
  title        = {{Separability criteria for the evaluation of boundary detection benchmarks}},
  url          = {{http://doi.org/10.1109/TIP.2015.2510284}},
  volume       = {{25}},
  year         = {{2016}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: