Open AccessSalient region growing based on Gaussian pyramid
Author(s) -
Jiao Jianjun,
Wang Xiaopeng,
Zhang Jungping,
Wang Qingsheng
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/ipr2.12307
Subject(s) - artificial intelligence , pyramid (geometry) , image segmentation , region growing , salient , pattern recognition (psychology) , segmentation , computer vision , computer science , gaussian , scale space segmentation , enhanced data rates for gsm evolution , path (computing) , mathematics , physics , geometry , quantum mechanics , programming language
For region growing image segmentation, seed selection and image noise are two major concerns causing negative segmentation performance. This paper proposes a regional growth algorithm based on the Gaussian pyramid (GPRG), which automatically selects seed points and optimizes the growth path. To achieve automatic seed selection, Gaussian difference pyramid generated by the Gaussian pyramid is required. The growing seed of the salient target in an image is acquired by searching for extremums in each image of the Gaussian difference pyramid. For growing path optimization, edge changing direction is updated according to different Gaussian difference pyramid images with sizes from small to large. The edge of region growing is determined by the edge of the salient target in an image. Segmentation experiments show that this algorithm is able to automatically select seeds and accurately segment the salient target in an image with and without image noise. Performances in terms of segmentation accuracy (SA), mean intersection‐over‐union (mIoU) and E‐measure are superior compared with other methods.