An active contour model driven by adaptive local pre-fitting energy function based on Jeffreys divergence for image segmentation
作者：
Highlights：
• Jeffreys divergence computes energy loss to reach boundary via degree of difference.
• An adaptive function is constructed to normalize the original data-driven term.
• A regularization function stretches the data in the vicinity of zero-level set.
• Pre-fitting functions are computed before update process to reduce computation time.
摘要
•Jeffreys divergence computes energy loss to reach boundary via degree of difference.•An adaptive function is constructed to normalize the original data-driven term.•A regularization function stretches the data in the vicinity of zero-level set.•Pre-fitting functions are computed before update process to reduce computation time.
论文关键词：Active contour model,Adaptive local pre-fitting,Jeffreys divergence,Level set method,Image segmentation
论文评审过程：Received 4 April 2022, Revised 8 July 2022, Accepted 7 August 2022, Available online 12 August 2022, Version of Record 19 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118493
