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Application of Chaos Cuckoo Search Algorithm in computer vision technology

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Abstract

Image segmentation is an essential phase in image analysis and computer vision. In computer vision, image processing denotes the analysis and handling of digital images to enhance their quality. Image processing becomes more challenging because of many complexes, noisy images from various sources in selecting applications, requiring minimal computational time/cost procedures and higher accuracy. Hence, in this paper, Chaos Cuckoo Search Algorithm (CCSA) has been suggested to resolve image segmentation and improve image accuracy. An un-deterministic problem is the challenge of unsure pixel detection and rim formulation for picture segmentation. The use of CV and optimization approaches has reduced the uncertainty about restricted picture characteristics. Ergodicity and stochasticity are formulated in this proposal which fulfill certain pixel and edge detection. Heterogeneous picture patterns solve the insecurity with chaotic maps and an Algorithm of Cuckoo Search. Unlike ordinary cuckoo searches, the chaotic mapping fuses deterministic search and stochastic validations. Chaos belongs to a characteristic of nonlinear models. Chaotic motion ensures homogeneity within a certain range since it has obsessed ergodicity, uncertainty, and stochasticity. The proposed model automates algorithms' settings and delivers optimal parameters for computer vision applications and image segmentation. Furthermore, a local search approach is utilized to enhance the outcomes in the cuckoo search algorithm. The experimental results show that the proposed CCSA model enhances accuracy and reduces uncertainty compared to other existing approaches.

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Notes

  1. https://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php.

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Authors and Affiliations

  1. School of Electrical and Information Technology, Suzhou Institute of Technology, Jiangsu University of Science and Technology, Zhangjiagang, 215600, China

    Jiayi Fan & Yi Huang

  2. Engineering Management Department, Zhangjiagang City Investment Co. Ltd, Zhangjiagang, 215600, China

    Wenjing Xu

  3. Faculty of Technology, Design and Environment, Visual Artificial Intelligence Lab, Oxford Brookes University, Oxford, UK

    R. Dinesh Jackson Samuel

Authors
  1. Jiayi Fan
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  2. Wenjing Xu
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  3. Yi Huang
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  4. R. Dinesh Jackson Samuel
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Contributions

Conception and design of study: JF, Acquisition of data: WX, Analysis and/or interpretation of data: RDJS, Drafting the manuscript: YH.

Corresponding author

Correspondence to Yi Huang.

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All authors declare that they have no conflict of interests.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by Vicente Garcia Diaz.

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Fan, J., Xu, W., Huang, Y. et al. Application of Chaos Cuckoo Search Algorithm in computer vision technology. Soft Comput 25, 12373–12387 (2021). https://doi.org/10.1007/s00500-021-05950-8

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  • DOI: https://doi.org/10.1007/s00500-021-05950-8

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