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access icon free Super-pixel image segmentation algorithm based on adaptive equalisation feature parameters

  • Author(s): Shifei Ding 1, 2 ; Lijuan Wang 1, 3 ; Lin Cong 1, 2
    • View affiliations
  • Source: Volume 14, Issue 17, 24 December 2020, p. 4461 – 4467
    DOI:  10.1049/iet-ipr.2020.0475 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 29/03/2020, Accepted 06/11/2020, Revised 17/10/2020, Published 04/12/2020

Image segmentation is a key step in the process of image data processing. The quality of image segmentation will directly affect the accuracy of image cognitive understanding. The purpose of image segmentation is to divide the image into regions with specific semantics. For the simple linear iterative clustering (SLIC) algorithm, the feature equalisation parameters need to be set manually during image segmentation, which results in the lack of segmentation effects and slow processing time. By introducing the theory of intermediary mathematics, an improved adaptive SLIC super-pixel algorithm is proposed, which can adaptive generate characteristic equalisation parameters according to the specific situation of the image, thereby simplifying the operation steps and improving the image segmentation effect. After experimental verification and analysis, compared with the original SLIC algorithm and several other super-pixel contrast algorithms, the algorithm in this study can effectively shorten the processing time and achieve a better segmentation effect.

Inspec keywords: pattern clustering; iterative methods; image segmentation; adaptive equalisers

Other keywords: super-pixel contrast algorithms; intermediary mathematics theory; adaptive equalisation feature parameters; super-pixel image segmentation algorithm; image data processing; improved adaptive SLIC super-pixel algorithm; simple linear iterative clustering algorithm; image segmentation effect; adaptive generate characteristic equalisation parameters; image cognitive understanding

Subjects: Data handling techniques; Optical, image and video signal processing; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques

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