Legendre Based Adaptive Image Segmentation Combining the Gradient Information

Jiajie ZHU, Bin FANG*, Mingliang ZHOU, Hengjun ZHAO, Futing LUO

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

In this paper, we propose an adaptive variable exponent level set method based on Legendre polynomials for object segmentation in complex visual environment. First, we use a set of Legendre basis functions to approximate the region intensity, which enable us to accommodate heterogeneous objects. Second, an improved function is presented to update exponent adaptively and ensures the image gradient information embedding into the model easily. The proposed method is robust to low contrast, blurred boundaries, noise and the 10-cation of initial contour, and sufficient in handling large scale intensity variations. Experimental results demonstrate that the proposed method can achieve relatively high segmentation accuracy and less computational time.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 : Proceedings
PublisherIEEE
Pages863-867
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sept 202028 Sept 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/09/2028/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Active contour
  • image segmentation
  • Legendre polynomial
  • level set

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