Simple global thresholding neural network for shadow detection

Shadow detection based on vision sensors is widely used in image processing. Because of the variability of illumination and projection surface color, shadow detection based on a color image is a challenging problem. Aiming at solving the conflict between the complexity and robustness of current shad...

全面介紹

Saved in:
書目詳細資料
Main Authors: Li, Guiyuan, Zong, Changfu, Zhang, Dong, Zhu, Tianjun, Li, Jianying
其他作者: School of Electrical and Electronic Engineering
格式: Article
語言:English
出版: 2022
主題:
在線閱讀:https://hdl.handle.net/10356/153722
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結:Shadow detection based on vision sensors is widely used in image processing. Because of the variability of illumination and projection surface color, shadow detection based on a color image is a challenging problem. Aiming at solving the conflict between the complexity and robustness of current shadow detection algorithms, we established a new shadow detection network by combining the global thresholding method with a neural network, which realized the decoupling of the global threshold and binary fusion. Three public shadow detection datasets, large-scale shadow dataset of Stony Brook University (SBU), large-scale dataset with image shadow triplets (ISTD), and shadow detection for mobile robots features evaluation and datasets (SDMR), were utilized for its verification. Experimental results show that the performance of the proposed network approaches that of previous deep learning methods, both visually and in terms of objective indicators, but the proposed network has the advantages of a simple structure and good robustness.