Skip to main content

Advertisement

Springer Nature Link
Log in

Flexible edge detection and its enhancement by smell bees optimization algorithm

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Edge detection still nowadays a complex challenge since the intrinsic proprieties of an image vary from one case to another. Thus, capturing the semantic content of an image relies only on the human interpretation. In this paper, a novel edge detection algorithm is proposed. Compared to usual edge detectors based on derivative filters, the idea behind the proposed algorithm is to extract edges by exploiting only information present in the image itself without need of any extra information. The used detection process is composed of two main phases, smoothing the image and extracting edges. Besides the simplicity of its implementation, the detection algorithm CMAX is doted to more flexibility enabling us to decide on the degree of details embedded in each region of the image independently. Also, as a complementary phase, the quality of detection can be improved by using an optimization approach based on the nature-inspired algorithm smell bees optimization. The quantitative evaluation results of CMAX before and after enhancement and their comparison with others well-known detectors are done by using the benchmark of Berkeley images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_code.tgz.

References

  1. Saber E, Tekalp A, Bozdagi G (1997) Fusion of color and edge information for improved segmentation and edge linking. Image Vis Comput 15(10):769–780

    Article  Google Scholar 

  2. Elder JH, Zucker SW (1998) Local scale control for edge detection and blur estimation. IEEE Trans Pattern Anal Mach Intell 20(7):699–716

    Article  Google Scholar 

  3. Fan J, Yau David KY, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10):1454–1466

    Article  Google Scholar 

  4. Basu M (2002) Gaussian-based edge-detection methods: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 32(3):252–260

    Article  Google Scholar 

  5. D’Elia C, Poggi G, Scarpa G (2003) A tree-structured markov random field model for bayesian image segmentation. IEEE Trans Image Process 12(10):1259–1273

    Article  MathSciNet  Google Scholar 

  6. Garcia Ugarriza L, Saber E, Vantaram SR, Amuso V, Shaw M, Bhaskar R (2009) Automatic image segmentation by dynamic region growth and multiresolution merging. IEEE Trans Image Process 18(10):2275–2288

    Article  MathSciNet  Google Scholar 

  7. Qin AK, David A (2010) Clausi multivariate image segmentation using semantic region growing with adaptive edge penalty. IEEE Trans Image Process 19(8):2157–2170

    Article  MathSciNet  Google Scholar 

  8. Wu Z, Lu X, Deng Y (2015) Image edge detection based on local dimension: a complex networks approach. Phys A Stat Mech Appl 440:9–18

    Article  Google Scholar 

  9. Abdulhussain SH, Ramli AR, Mahmmod BM, Al-Haddad SAR, Jassim WA (2017) Image edge detection operators based on orthogonal polynomials. Proc of the Int J Image Data Fusion 8(3):293–308

    Google Scholar 

  10. Biswas S, Hazra R (2018) Robust edge detection based on modified Moore-Neighbor. Optik 168:931–943

    Article  Google Scholar 

  11. Medjram S, Babahenini MC, Taleb-Ahmed A, Ali YMB (2018) Automatic hand detection in color images based on skin region verification. Multimed Tools Appl 77(11):13821–13851

    Article  Google Scholar 

  12. Mittal M et al (2019) An efficient edge detection approach to provide better edge connectivity for image analysis. IEEE Access 7:33240–33255

    Article  Google Scholar 

  13. Raheja S, Kumar A (2019) Edge detection based on type-1 fuzzy logic and guided smoothening. Evol Syst

  14. Eser SERT, Derya AVCI (2019) A new edge detection approach via neutrosophy based on maximum norm entropy. Expert Syst Appl 115:499–511

    Article  Google Scholar 

  15. Orujov F, Maskeliūnas R, Damaševičius R, Wei W (2020) Fuzzy based image edge detection algorithm for blood vessel detection in retinal images. Appl Soft Comput 94:106452

    Article  Google Scholar 

  16. Bhandarkar SM, Zhang Y, Potter WD (1994) An edge detection technique using genetic algorithm-based optimization. Pattern Recogn 27(9):1159–1180

    Article  Google Scholar 

  17. Xiao-Dong Zhuang (2004) Edge feature extraction in digital images with the ant colony system. In Proc IEEE Conf Comput Intell Meas Syst Appl, pp. 133–136

  18. Ali YMB (2009) Edge-based Segmentation using Robust evolutionary algorithm applied to medical images. J Signal Process Syst 54(1–3):231–238

    Google Scholar 

  19. Alipoor M, Imandoost S, Haddadnia J (2010) Designing edge detection filters using particle swarm optimization. In Proceedings of 18th Iran Conference on Electrical Engineering, pp. 548–552.18

  20. Elaiza N, Khalid A, Manaf M (2010) Performance of optimized fuzzy edge detectors using particle swarm algorithm. Adv Swarm Intell Lect Notes in Comp Sci 6145:175–182

    Article  Google Scholar 

  21. Setayesh M, Zhang M, Johnston M (2011) Detection of continuous, smooth and thin edges in noisy images using constrained particle swarm optimization. In Proceedings of 13th annual Conference on Genetic and Evolutionary Computation, pp. 45–52. http://dl.acm.org/author_page.cfm?id=81486655245&coll=DL&dl=ACM&trk=0&cfid=199629259&cftoken=95048000

  22. Hassanzadeh T, Vojodi H, Mahmoudi F (2011) Non-linear grayscale image enhancement based on firefly Algorithm. Proc Swarm Evol Memetic Comput (SEMCCO) Lect Notes Comput Sci 7077:174–181

    Article  Google Scholar 

  23. Setayesh M (2011) Edge detection using constrained discrete particle swarm optimisation in noisy images. In Proceedings of IEEE congress on evolutionary computation, pp. 246–253

  24. Wenlong F, Johnston M, Mengjie Z (2012) Soft edge maps from edge detectors evolved by genetic programming. In Proceedings of IEEE Congress on Evolutionary Computation, pp. 1–8

  25. Xie S, Tu Z (2015) Holistically-nested edge detection. In: Proceedings of IEEE International Conference on Computer Vision, 2015.

  26. Yu Z, Feng C, Liu MY (2017) CASENet: Deep category-aware semantic edge detection. In: Proceedings of the 30th IEEE international conference on computer vision and pattern recognition, 2017

  27. Senthikumar R, Bharathi A, Sowmya B, Sugunamuki KR (2018) Image segmentation edge detection techniques using—soft computing approaches. In: Proceedings of the IEEE International Conference on Soft-Computing and Network and Network Security, 2018

  28. Dagara NS, Dahiyab PK (2020) Edge detection technique using binary particle swarm optimization. Procedia Comput Sci 167:1421–1436

    Article  Google Scholar 

  29. Ali YMB (2019) Smell Bees optimization for new embedding steganographic scheme in spatial domain. Swarm Evolut Comput 44:584–596

    Article  Google Scholar 

  30. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the 8th International Conference on Computer Vision, 2: 416–423

  31. Lopez-Molina C, De Baets B, Bustince H (2013) Quantitative error measures for edge detection. Pattern Recogn 46(4):1125–1139

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Badji Mokhtar, BP 12, 23000, Annaba, Algeria

    Yamina Mohamed Ben Ali

Authors
  1. Yamina Mohamed Ben Ali
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yamina Mohamed Ben Ali.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohamed Ben Ali, Y. Flexible edge detection and its enhancement by smell bees optimization algorithm. Neural Comput & Applic 33, 10021–10041 (2021). https://doi.org/10.1007/s00521-021-05769-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05769-2

Keywords