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Various Techniques of Image Segmentation

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1380))

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Abstract

Image segmentation techniques are essential for any kind of digital image or picture analysis. This paper suggests different methods of image segmentation like threshold, clustering, matching, and edge. Additionally, one method is proposed whose goal is to make the features of image usable to create segments for efficient processing techniques like recognition or compression. This method is used to find the objects and the images edges. Images are segmented or divided into components on the basis of similar characteristics of the pixels. Further, results obtained from the proposed method are a set of outlines that are taken out from the image by setting precise value of threshold.

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References

  1. Anghelescu, P., Iliescu, V.G., Mara, C., Gavriloaia, M.: Automatic thresholding method for edge detection algorithms. In: 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4. IEEE, Ploiesti, Romania (2016). https://doi.org/10.1109/ECAI.2016.7861099

  2. Ju, Z.-W., Chen, J.-Z., Zhou, J.-L.: Image segmentation based on edge detection using K-means and an improved ant colony optimization. In: International Conference on Machine Learning and Cybernetics, pp. 297–303. IEEE, Tianjin (2013). https://doi.org/10.1109/ICMLC.2013.6890484

  3. Jain, N., Lala, A.: Image segmentation: a short survey. In: 4th International Conference on the Next Generation Information Technology Summit, pp. 380–384. IEEE, Noida, India (2013). https://doi.org/10.1049/cp.2013.2345

  4. Li, Z., Yang, Z., Wang, W., Cui, J.: An adaptive threshold edge detection method based on the law of gravity. In: 25th Chinese Control and Decision Conference, pp. 897–900. IEEE, Guiyang, China (2013). https://doi.org/10.1109/CCDC.2013.6561050

  5. Lei, W., Man, M., Shi, R., Liu, G., Gu, Q.: Target detection based on automatic threshold edge detection and template matching algorithm in GPR. In: IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, pp. 1406–1410. IEEE, Chongqing, China (2018). https://doi.org/10.1109/IAEAC.2018.8577508

  6. Mo, S., Gan, H., Zhang, R., Yan, Y., Liu, X.: A novel edge detection method based on adaptive threshold. In: IEEE 5th Information Technology and Mechatronics Engineering Conference, pp. 1223–1226. IEEE, Chongqing, China (2020). https://doi.org/10.1109/ITOEC49072.2020.9141577

  7. Thakkar, M., Shah, H.: Edge detection techniques using fuzzy thresholding. In: World Congress on Information and Communication Technologies, pp. 307–312. IEEE, Mumbai, India (2011). https://doi.org/10.1109/WICT.2011.6141263

  8. ElAraby, W.S., Madian, A.H., Ashour, M.A., Farag, I., Nassef, M.: Fractional edge detection based on genetic algorithm. In: 29th International Conference on Microelectronics, pp. 1–4. IEEE, Beirut, Lebanon (2017). https://doi.org/10.1109/ICM.2017.8268860

  9. Li, Z., Wang, J.: An adaptive corner detection algorithm based on edge features. In: 10th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 191–194. IEEE, Hangzhou, China (2018). https://doi.org/10.1109/IHMSC.2018.10150

  10. Jie, G., Ning, L.: An improved adaptive threshold canny edge detection algorithm. In: International Conference on Computer Science and Electronics Engineering, pp. 164–168. IEEE, Hangzhou, China (2012). https://doi.org/10.1109/ICCSEE.2012.154

  11. Liang, Y., Zhang, M., Browne, W.N.: Image segmentation: a survey of methods based on evolutionary computation. In: Dick, G., et al. (eds.) Simulated Evolution and Learning. Lecture Notes in Computer Science, vol. 8886, pp. 847–859. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13563-2_71

  12. Chouhan, S.S., Kaul, A., Singh, U.P.: Image segmentation using computational intelligence techniques: review. Arch. Comput. Meth. Eng. 26, 533–596 (2019). https://doi.org/10.1007/s11831-018-9257-4

    Article  MathSciNet  Google Scholar 

  13. Liu, X., Deng, Z., Yang, Y.: Recent progress in semantic image segmentation. Artif. Intell. Rev. 52, 1089–1106 (2019). https://doi.org/10.1007/s10462-018-9641-3

    Article  Google Scholar 

  14. De, S., Bhattacharyya, S., Chakraborty, S., Dutta, P.: Image segmentation: a review. In: Hybrid Soft Computing for Multilevel Image and Data Segmentation. Computational Intelligence Methods and Applications, pp. 29–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47524-0_2

  15. Suresh, K., Srinivasa Rao P.: Various image segmentation algorithms: a survey. In: Satapathy, S., Bhateja, V., Das, S. (eds.) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol. 105, pp. 233–239. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1927-3_24

  16. Bilbro, G.L., White, M., Snyder, W.: Image segmentation with neurocomputers. In: Eckmiller, R., v.d. Malsburg, C. (eds.) Neural Computers. Springer Study Edition, vol. 41, pp. 71–79. Springer, Berlin, Heidelberg (1989). https://doi.org/10.1007/978-3-642-83740-1_9

  17. Chouhan, S.S., Kaul, A., Singh, U.P.: Soft computing approaches for image segmentation: a survey. Multimedia Tools Appl. 77, 28483–28537 (2018). https://doi.org/10.1007/s11042-018-6005-6

    Article  Google Scholar 

  18. Dautaniya, A.K., Sharma, V.: High-performance fuzzy C-means image clustering based on adaptive frequency-domain filtering and morphological reconstruction. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds.) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 1053, pp. 1221–1234. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0751-9_112

  19. Kalbande, D.R., Khopkar, U., Sharma A., Daftary, N., Kokate, Y., Dmello, R.: Early stage detection of psoriasis using artificial intelligence and image processing. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds.) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 1053, pp. 1199–1208. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0751-9_110

  20. Sharma, M.S., Sharma, J., Atre, D., Tomar, R.S., Shrivastava, N.: Image fusion and its separation using SVD-based ICA method. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds.) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 1053, pp. 933–946. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0751-9_87

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Author information

Authors and Affiliations

  1. Amity Institute of Information Technology, Amity University, Noida, India

    Reshu Agarwal

  2. G.L. Bajaj Institute of Technology and Management, Greater Noida, India

    Annu Malik, Tanya Gupta & Shylaja VinayKumar Karatangi

Authors
  1. Reshu Agarwal
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  2. Annu Malik
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  3. Tanya Gupta
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  4. Shylaja VinayKumar Karatangi
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Shobhit University Gangoh, Gangoh, Uttar Pradesh, India

    Tarun K. Sharma

  2. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Chang Wook Ahn

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Agarwal, R., Malik, A., Gupta, T., Karatangi, S.V. (2022). Various Techniques of Image Segmentation. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_49

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