The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Edge Detection Techniques For Image Segmentation
Open accessAbstract
Interpretation of image contents is one of the objectives in computer vision specifically in image processing.In this era it has received much awareness of researchers.In image interpretation the partition of the image into object and background is a severe step.Segmentation separates an image into its component regions or objects.Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully.In this context, edge detection is a fundamental tool for image segmentation.In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.
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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
Digital Image Processing Using MATLAB
González, R. C., Woods, R. E., & Eddins, S. L. (2003) · Cited by 7,079
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
Robust Regression and Outlier Detection
Piepel, G. F. (1989) · Technometrics · doi:10.1080/00401706.1989.10488524 · Cited by 6,144
A review on image segmentation techniques
Pal, N. R., & Pal, S. K. (1993) · Pattern Recognition · doi:10.1016/0031-3203(93)90135-j · Cited by 3,708
A Computational Approach to Edge Detection
CANNY, J. (1987) · Readings in Computer Vision · doi:10.1016/b978-0-08-051581-6.50024-6 · Cited by 2,275
Finding Edges and Lines in Images
Canny, J. (1983) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 833
Robust regression methods for computer vision: A review
Meer, P., Mintz, D., Rosenfeld, A., & Kim, D. Y. (1991) · International Journal of Computer Vision · doi:10.1007/bf00127126 · Cited by 638
Robust Regression and Outlier Detection.
Kafadar, K., Rousseeuw, P. J., & Leroy, A. M. (1989) · Journal of the American Statistical Association · doi:10.2307/2289958 · Cited by 621
Computer determination of the constituent structure of biological images
Kirsch, R. A. (1971) · Computers and Biomedical Research · doi:10.1016/0010-4809(71)90034-6 · Cited by 606
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A review of semantic segmentation using deep neural networks
Guo, Y., Liu, Y., Georgiou, T., & Lew, M. S. (2018) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-017-0141-z · Cited by 882
Kheradmandi, N., & Mehranfar, V. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.126162 · Cited by 426
Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
Metadata
- Authors
- Muthukrishnan, R., & Radha, M.
- Venue
- International Journal of Computer Science and Information Technology
- Published
- 2011-12-31
- Volume
- 3
- Issue
- 6
- Pages
- 259-267
- ISSN
- 0975-3826 (ISSN-L)
- 0975-4660
- OpenAlex
- W2114563034
- Cited by
- 419
- References
- 14
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- bronze
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