Open AccessLevel set method with Retinex‐corrected saliency embedded for image segmentation
Author(s) -
Liu Dongmei,
Chang Faliang,
Zhang Huaxiang,
Liu Li
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12123
Subject(s) - artificial intelligence , color constancy , segmentation , computer science , computer vision , image segmentation , pattern recognition (psychology) , level set (data structures) , set (abstract data type) , enhanced data rates for gsm evolution , scale space segmentation , image (mathematics) , boundary (topology) , mathematics , mathematical analysis , programming language
Abstract It can be a very challenging task when using level set method segmenting natural images with high intensity inhomogeneity and complex background scenes. A new synthesis level set method for robust image segmentation based on the combination of Retinex‐corrected saliency region information and edge information is proposed in this work. First, the Retinex theory is introduced to correct the saliency information extraction. Second, the Retinex‐corrected saliency information is embedded into the level set method due to its advantageous quality which makes a foreground object stand out relative to the backgrounds. Combined with the edge information, the boundary of segmentation will be more precise and smooth. Experiments indicate that the proposed segmentation algorithm is efficient, fast, reliable, and robust.