The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Image segmentation evaluation: a survey of methods
Open accessAbstract
Image segmentation is a prerequisite for image processing. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. How to effectively evaluate the quality of image segmentation is very important. In this paper, the existing image segmentation quality evaluation methods are summarized, mainly including unsupervised methods and supervised methods. Based on hot issues, the application of metrics in natural, medical and remote sensing image evaluation is further outlined. In addition, an experimental comparison for some methods were carried out and the effectiveness of these methods was ranked. At the same time, the effectiveness of classical metrics for remote sensing and medical image evaluation is also verified.
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Cites
Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool
Taha, A. A., & Hanbury, A. (2015) · BMC Medical Imaging · doi:10.1186/s12880-015-0068-x · Cited by 2,920
A survey of thresholding techniques
Sahoo, P. K., Soltani, S., & Wong, A. K. C. (1988) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(88)90022-9 · Cited by 2,662
MatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444
Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659
Learning to compare image patches via convolutional neural networks
Zagoruyko, S., & Komodakis, N. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299064 · Cited by 1,140
A survey on evaluation methods for image segmentation
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Image segmentation evaluation: A survey of unsupervised methods
Zhang, H., Fritts, J. E., & Goldman, S. A. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.08.003 · Cited by 963
Toward Objective Evaluation of Image Segmentation Algorithms
Unnikrishnan, R., Pantofaru, C., & Hebert, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1046 · Cited by 792
Unsupervised feature learning framework for no-reference image quality assessment
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MESH: measuring errors between surfaces using the Hausdorff distance
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Cited by
Yeung, M., Sala, E., Schönlieb, C.-B., & Rundo, L. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102026 · Cited by 733
Sriraam, N., Punyaprabha, V., Sushma, T. V., & Suresh, S. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02814-1 · Cited by 5
Metadata
- Authors
- Wang, Z., Wang, E., & Zhu, Y.
- Venue
- Artificial Intelligence Review
- Published
- 2020-04-18
- Volume
- 53
- Issue
- 8
- Pages
- 5637-5674
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3017097154
- Cited by
- 294
- References
- 94
- FWCI
- 12.95
- Type
- journal article
- Language
- en
- Open access
- green
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