The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A robust visual method for assessing the relative performance of edge-detection algorithms
Abstract
A new method for evaluating edge detection algorithms is presented and applied to measure the relative performance of algorithms by Canny, Nalwa-Binford, Iverson-Zucker, Bergholm, and Rothwell. The basic measure of performance is a visual rating score which indicates the perceived quality of the edges for identifying an object. The process of evaluating edge detection algorithms with this performance measure requires the collection of a set of gray-scale images, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and applying statistical analysis methods. The novel aspect of this work is the use of a visual task and real images of complex scenes in evaluating edge detectors. The method is appealing because, by definition, the results agree with visual evaluations of the edge images.
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Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Intraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532
Cited by
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Computer-Vision-Based Fabric Defect Detection: A Survey
Kumar, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.1930.896476 · Cited by 725
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“[172]: (a) the algorithm itself, (b) the algorithm parameters used in the evaluation, (c) the method used for evaluating the algorithm, (d) the nature of images used to measure the performance of the algorithm.”
MethodStand-Alone Objective Segmentation Quality Evaluation
Correia, P. L., & Pereira, F. (2002) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865702000707 · Cited by 35
Metadata
- Authors
- Heath, M. D., Sarkar, S., Sanocki, T., & Bowyer, K. W.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997-12
- Volume
- 19
- Issue
- 12
- Pages
- 1338-1359
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2124592837
- Cited by
- 494
- References
- 2
- FWCI
- 15.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Heath, M. D., Sarkar, S., Sanocki, T., & Bowyer, K. W. (1997). A robust visual method for assessing the relative performance of edge-detection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.643893
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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