Abstract
In digital images, edges characterize object boundaries, then their detection remains a crucial stage in numerous applications. To achieve this task, many edge detectors have been designed, producing different results, with different qualities. Evaluating the response obtained by these detectors has become a crucial task. In this paper, several referenced-based boundary detection evaluations are detailed, pointing their advantages and disadvantages through concrete examples of edge images. Then, a new supervised edge map quality measure is proposed, comparing a ground truth contour image, the candidate contour image and their associated spacial nearness. Compared to other boundary detection assessments, this new method has the advantage to be normalized and remains a more reliable edge map quality measure.
Keywords
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The authors wish to thank the Iraqi Ministry of Higher Education and Scientific Research for funding and supporting this work.
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Abdulrahman, H., Magnier, B., Montesinos, P. (2017). A New Normalized Supervised Edge Detection Evaluation. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_23
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DOI: https://doi.org/10.1007/978-3-319-58838-4_23
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