Abstract
The problem of edge detection plays a crucial role in almost all research areas of image processing. If edges are detected accurately, one can detect the location of objects and the parameters such as shape and area can be measured more precisely. In order to overcome the above problem, a feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges. The major contributions of the present work are summarized in three steps. In the first step, a restoration-based enhancement algorithm is proposed to get a fine image from a distorted noisy image. In the second step, two versions of input images are registered using a modified FBIR approach. In the third step, to overcome the drawback of canny edge detection algorithm, each step of the algorithm is modified. The output is then fed to a “fuzzy inference system”. The “fuzzy rule-based technique”, when applied to the problem of “edge detection”, is very “efficient” because the thickness of the edges can be controlled by simply changing “rules and output parameters”. The domain of the images under consideration is various well-known image databases such as Berkeley and USC-SIPI databases, whereas the proposed method is also suitable for other types of both indoor and outdoor images. The robustness of the proposed method is analysed, compared and evaluated with seven image assessment quality (IAQ) parameters. The performance of the proposed method is compared with some of the state-of-the-art edge detection methods in terms of the seven IAQ parameters.
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Katiyar, S.K., Arun, P.V.: Comparative analysis of common edge detection techniques in context of object extraction. IEEE TGRS 50(11b), 68–79 (2014)
Becerikli, Y., Karan, T.M., Cabestany, J., Prieto, A., Sandoval, D.F.: A New Fuzzy Approach for Edge Detection, pp. 943–951. Springer, Berlin (2005)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–697 (1986)
Zitova, J.F., Sroubek, F.: Image Registration: A Survey and Recent Advances. Institue of Information Theory and Automation Academy of Sciences of Czech Republic Pod Uarenskou vezi4, 18208 Prague and Czech Republic (ICIP Tutorial) (2005)
Kumawat, A., Panda, S.: Feature detection and description in remote sensing images using a hybrid feature detector. In: International Conference on Computational Intelligence and Data Science (ICCIDS), Procedia Computer Science, vol. 132, pp. 277–287 (2018)
Kumawat, A., Panda, S.: Feature extraction and matching of river-dam images in ODISHA using a novel feature detector. In: International Conference on Computational Intelligence in Pattern Recognition (CIPR). Springer (2018)
Bhardwaj, S., Mittal, A.: A survey on various edge detector techniques. Proc. Technol. 4, 220–226 (2012)
El-Khamy, S.E., Lotfy, M., El-Yamany, N.: A modied fuzzy Sobel edge detector. In: Proceedings of IEEE 17th National Radio Science Conference, pp. 1–9 (2000)
Gonzalez, R., Woods, R.: Digital image processing. 2nd Edition, Prentice Hall, Upper Saddle River (2002)
Xuan, L., Hong, Z.: An improved CANNY edge detection algorithm. In: Proceedings of 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 275–278 (2017)
Mittal, M. et al.: An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis. IEEE Access 7, 33240–33255 (2019). https://doi.org/10.1109/ACCESS.2019.2902579
Peli, T., Malah, D.: A study of edge detection algorithms. Comput. Graph. Image Process. 20, 1–21 (1982)
Torre, V., Poggio, T.A.: On edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(2), 163–187 (1986)
Argyle, E.: Techniques for edge detection. Proc. IEEE 59, 285–286 (1971)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 207(1167), 187–217 (1980)
Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.W.: Comparison of edge detectors: a methodology and initial study. Comput. Vis. Image Underst. 69(1), 38–54 (1998)
Sharifi, M., Fathy, M., Mahmoudi, M. T.: A classified and comparative study of edge detection algorithms. In: Proceedings. International Conference on Information Technology: Coding and Computing, pp. 117–120 (2002). https://doi.org/10.1109/ITCC.2002.1000371
Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1351–1365 (2001). https://doi.org/10.1109/34.977560
Mathura, N., Mathur, S., Mathur, D.: A novel approach to improve Sobel edge detector. In: 6th (ICACC) International Conference on Advances in Computing and Communications (2016)
Samant, S., Salvi, M., Sabuwala, M.H.: Edge detection based on fuzzy logic. Int. J. Sci. Eng. Res. 5(9), 266 (2014)
Melin, P., Gonzalez, C. I., Castro, J. R., Mendoza O., Castillo, O.: Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic. IEEE Trans Fuzzy Syst 22(6), 1515–1525 (2014). https://doi.org/10.1109/TFUZZ.2013.2297159
Pugina, E.V., Zhiznyakova, A.L.: Edge detection in remote sensing images based on fuzzy image representation. In: 3rd International Conference on Information Technology and Nanotechnology (2017)
Moya-Albor, E., Ponce, H., Brieva, J.: An edge detection method using a fuzzy ensemble approach. Acta Polytech. Hung. 14(3), 149–168 (2017)
Alshennawy, A.A., Aly, A.A.: Edge detection in digital images using fuzzy logic technique. World Acad. Sci. Eng. Technol. 5(4), 264–269 (2009)
Aborisade, D.O.: Fuzzy logic based digital image edge detection, global. J. Comput. Sci. Technol. 10(14), 78–83 (2010)
Kaur, G.: Image enhancement and its techniques, a review. Int. J. Comput. Trends Technol. (IJCTT) 3(12), 148–151 (2014)
Abdullah-Ai-Nahid, Khan, T.M., Kong, Y.: Performance analysis of integrated canny and fuzzy-logic based (2-by-2 cell block) edge-detection algorithms. In: UKSim-AMSS 10th European Modelling Symposium on Computer Modelling and Simulation, pp. 64–69. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ (2016). https://doi.org/10.1109/EMS.2016.021
Chung, I. Chen, Y. Pal, N. R.: Feature Selection With Controlled Redundancy in a Fuzzy Rule Based Framework. IEEE Trans Fuzzy Syst 26(2), 734–748. https://doi.org/10.1109/TFUZZ.2017.2688358
Banerjee, A., Das, N. Santosh, K.C.: Weber local descriptor for image analysis and recognition: a survey. Vis Comput (2020). https://doi.org/10.1007/s00371-020-02017-x
Ahmad, S., Khan, M.F.: Multimodal non-rigid image registration based on elastodynamics. J. Vis. Comput. 34, 21–27 (2018)
Gonzalez, C.I., Melin, P., Castro, J.R., Castillo, O.: Edge detection approach based on type-2 fuzzy images. J. Multiple Valued Log. Soft Comput. 33, 431–458 (2019)
Ontiveros-Robles, E., Melin, P., Castillo, O., Gonzalez, J.: Design and FPGA implementation of real-time edge detectors based on interval type-2 fuzzy systems. J. Multiple Valued Log. Soft Comput. 33, 295–320 (2019)
Martínez, G.E., Gonzalez, C.I., Mendoza, O., Melin, P.: General Type-2 Fuzzy Sugeno Integral for Edge Detection. J Imag 5(8), 71 (2019). https://doi.org/10.3390/jimaging5080071
Gonzalez, C.I., Melin, P., Castro, J.R., Castillo, O., Mendoza, O.: Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)
Gonzalez, C.I., Melin, P., Castro, J.R., Mendoza, O., Castillo, O.: An improved Sobel edge detection method based on generalized type-2 fuzzy logic. Soft Comput. 20(2), 773–784 (2016)
Renjie, S., Ziqi, Z., Haiyang, L.: Edge connection based Canny edge detection algorithm. Pattern Recognit. Image Anal. 27, 740–747 (2017)
Dhivya, R., Prakash, R.: Edge detection of satellite image using fuzzy logic. Cluster Comput. 22, 11891–11898 (2019)
Anas, E.: Edge detection techniques using fuzzy logic. In: 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (2016)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)
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Kumawat, A., Panda, S. A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL). Vis Comput 38, 3681–3702 (2022). https://doi.org/10.1007/s00371-021-02196-1
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DOI: https://doi.org/10.1007/s00371-021-02196-1