Total views : 329
An Automatic Method for Edge Detection Evaluation based on Semi-Optimal Edge Detector
Background/Objective: Edge detection is considered as one of the most important fields in extracting critical features in an automatic image analysis. Edge detection has several methods in the sides of global, and the evaluation of these methods in perfect way is not available as it can be in an automatic way. Methods/Analysis: This paper displays a new process based on the one of the most dynamic techniques for new automatic edge detection evaluation based on semi-optimal edge detector. The main advantages of the proposed method are the evaluation of any edge detection methods with results to know which the best edge detection technique is. Findings: This paper shows an automatic experimental evaluation results for each technique of edge detection by the results of the algorithm with several preferable edge detection methods, like Sobel, Roberts, Prewitt, Laplacian of Gaussian (LOG) and Canny to get real images. After that, applying standard deviation with median filter to smooth image and get rid of the noisy pixel to perform an ideal images. Improvement: Finally, applying Pratt measure for each method of edge detection separately used to get the final results of the evaluation algorithm in terms of an automatic method for edge detection evaluation based on semi-optimal edge detector.
Edge detection, Laplacian of Gaussian, Median Filter, Standard Deviation.
- Fredrik B. Edge focusing. Proceedings 8th Int Conf Pattern Recognition, France, 1986. p. 597–600.
- Sushil S, Aruna K. Various methods for edge detection in digital image processing. International Journal of Computer Science and Technology (IJCST). 2011; 2(2):188–90.
- Viergever M, Stiehl H, Klette R, Vincken K. Performance Characterization and Evaluation of Computer Vision Algorithms. Kluwer Academic. 2000.
- Garca N, Poyato A, Carnicer R, Cuevas F. Automatic generation of consensus ground thuth for the comparison of edge detection techniques. Image and Vision Computing. 2008; 26(4):496–511.
- Sujatha P, Sudha K. Performance analysis of different edge detection techniques for image segmentation. Indian Journal of Science and Technology. 2015 Jul; 8(14). Doi:10.17485/ijst/2015/v8i14/72946.
- Abdou I, Pratt W. Quantitative design and evaluation of enhancement/thresholding edge detectors. Proceedings of the IEEE. 1979; 67(5):753–66.
- Bowyer K, Kranenburg C, Dougherty S. Edge detector evaluation using empirical ROC curves. Computer Vision and Image Understanding. 2001 Oct; 84(1):77–103.
- Sridevi S, Nirmala S, Nirmaladevi S. Binary connectedness based ANT algorithm for ultrasound image edge detection. Indian Journal of Science and Technology. 2015 Jun; 8(12). Doi:10.17485/ijst/2015/v8i12/71964.
- Firas J. Semi-optimal edge detector based on simple standard deviation with adjusted thresholding. International Journal of Computer Applications. 2013; 68(2):43–8.
- Saleena Y, Dharun V. A new improved multistage eight directional median filter. Indian Journal of Science and Technology. 2015 Oct; 8(26). Doi:10.17485/ijst/2015/v8i26/81053.
- Pratt W. Digital Image Processing. Second Edition, J. Wiley & Sons, New York, 1991.
- Thirilogasundari V, Suresh V, Agatha S. Fuzzy based salt and pepper noise removal using adaptive switching median filter. Procedia Engineering. 2012; 38:2858–65.
- Gonzalez R, Woods R. Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2001.
- Bovik A, Huang T, Munson D. Edge-sensitive image restoration using order constrained least squares methods. IEEE Trans Acoust, Speech, Signal Processing. 1985; 33(1):1253–63.
- Kirsch R. Computer determination of the constituent structure of biomedical images. Comput Eiorned Res. 1971; 4(3):315–28.
- Boaventure I, Gonzaga A. Method to evaluate the performance of edge detector. Proceedings of The Brazilian Symposium on Computer Graphics and Image Processing, Brazi. 2009.
- Trucco, Jain et al. Edge detection, Chapter 4 and 5, 1982; 1–29.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.