Image Edge-Segmentation Techniques : A Review
DOI:
https://doi.org//10.32628/IJSRSET218528Keywords:
Segmentation, Edge-Preserving, Image ProcessingAbstract
Image segmentation is commonly applied technique in different domains such as automatic pattern recognition, image retrieval based content, machine vision, face detection, medical imaging, and object detection. Image segmentation involves classifying or identifying sub patterns in a given image. Many of algorithms and techniques for image segmentation have been proposed to optimize segmentation problems in a specific application area. In this work, different image segmentation techniques had been applied (threshold based, region based segmentation and edge based preserving methods. This Experiment have been done using MATLAB R2018b. Different edge detection methods such as Sobel, Prewitt, Roberts, Laplacian, Kiresh and Canny methods are performed on the benchmark image and the performance is analyzed with respect to the standard measure peak signal-to-noise ratio (PSNR), and mean square error. The results present that the Laplacian method is more effective than the other methods.
References
- Venmathi, A. R., Ganesh, E. N., & Kumaratharan, N. (2016). Kirsch compass Kernel edge detection algorithm for micro calcification clusters in mammograms. Middle-East Journal of Scientific Research, 24(4), 1530-1535. .https://doi.org 10.5829/idosi.mejsr.2016.24.04.23384.
- Saleh, B. J., Saedi, A. Y. F., al-Aqbi, A. T. Q., & abdalhasan Salman, L. (2021). Optimum Median Filter Based on Crow Optimization Algorithm. Baghdad Science Journal, 18(3), 0614-0614. https://doi.org/10.21123/bsj.2021.18.3.0614
- Da Rugna, J., Chareyron, G., & Konik, H. (2011, October). About segmentation step in content-based image retrieval systems. In World Congress on Engineering and Computer Science (pp. 550-554). https://doi.org/10.1007/978-3-319-69137-4_17
- Dhankhar, P., & Sahu, N. (2013). A review and research of edge detection techniques for image segmentation. International Journal of Computer Science and Mobile Computing, 2(7), 86-92.
- Maini, R., & Aggarwal, H. (2009). Study and comparison of various image edge detection techniques. International journal of image processing (IJIP), 3(1), 1-11.
- Biswas, R., & Sil, J. (2012). An improved canny edge detection algorithm based on type-2 fuzzy sets. Procedia Technology, 4, 820-824. https://doi.org/10.1016/j.protcy.2012.05.134
- Mageswari, S. U., Sridevi, M., & Mala, C. (2013). An experimental study and analysis of different image segmentation techniques. Procedia engineering, 64. https://doi.org/10.1016/j.proeng.2013.09.074
- Al-Amri, S. S., Kalyankar, N. V., & Khamitkar, S. D. (2010). Image segmentation by using edge detection. International journal on computer science and engineering, 2(3), 804-807.
- https://en.wikipedia.org/wiki/Sobel_operator.
- Singh, S., & Datar, A. (2013). EDGE detection techniques using Hough transform. International Journal of Emerging Technology and Advanced Engineering, 3(6), 333-337.
- Savant, S. (2014). A review on edge detection techniques for image segmentation. International Journal of Computer Science and Information Technologies, 5(4), 5898-5900.
- Jain, P., & Tyagi, V. (2016). A survey of edge-preserving image denoising methods. Information Systems Frontiers, 18(1), 159-170. https://en.wikipedia.org/wiki/Kirsch_operator. https://doi.org/10.1007/s10796-014-9527-0
- Gupta, S., Gupta, C., & Chakarvarti, S. K. (2013). Image Edge Detection: A Review. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2(7).
- Rashidha, R., & Simon, P. (2016). An adaptive-size median filter for impulse noise removal using neural network-based detector. International Journal of Signal and Imaging Systems Engineering, 9(4-5), 305-310. https://doi.org/10.1504/IJSISE.2016.078254
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