Robust regression for image binarization under heavy noise and nonuniform background
作者：
Highlights：
• This paper advances the background subtraction approach for image binarization.
• Our approach formulates a robust regression to estimate an image background.
• The proposed approach does not require any prior identification of edge pixels.
• The propose threshold selector binarizes noisy images better after background subtraction.
• The approach was validated with 26 benchmark images, comparing to nine existing methods.
摘要
•This paper advances the background subtraction approach for image binarization.•Our approach formulates a robust regression to estimate an image background.•The proposed approach does not require any prior identification of edge pixels.•The propose threshold selector binarizes noisy images better after background subtraction.•The approach was validated with 26 benchmark images, comparing to nine existing methods.
论文关键词：Image binarization,Background subtraction,Robust regression,Document image analysis,Microscopy image analysis
论文评审过程：Received 23 August 2017, Revised 3 April 2018, Accepted 4 April 2018, Available online 5 April 2018, Version of Record 13 April 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.04.005
