Open AccessEvaluation criterion of underwater object clustering segmentation with pulse‐coupled neural network
Author(s) -
Wang Xingmei,
Li Qiming,
Yu Yue,
Xu Yichao
Publication year - 2020
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/iet-ipr.2019.1662
Subject(s) - artificial intelligence , computer science , segmentation , sonar , underwater , cluster analysis , pixel , artificial neural network , computer vision , image segmentation , object (grammar) , noise (video) , pattern recognition (psychology) , channel (broadcasting) , image (mathematics) , geography , telecommunications , archaeology
The success of clustering algorithms in object segmentation depends on the quality of the evaluation criterion. However, sonar images are seriously affected by noise. Most of the existing evaluation criteria such as the Davies Bouldin (DB) criterion only considers their pixel value, and sonar image information extraction is not sufficient. As a result, they fail to achieve good underwater object segmentation results. To overcome this problem, this study proposes an improved DB criterion with pulse ‐coupled neural network (PCNN), which is called the DB‐PCNN. In the calculation process of DB‐PCNN, the role of internal activity items in PCNN is considered, which can make better use of pixel information in adjacent space on the sonar image. The experimental results show that DB‐PCNN can further improve the accuracy of underwater object segmentation and has certain adaptability to different optimisation frameworks.