@article{Liang2020, 
author = {Yongqing Liang and Navid Jafari and Xing Luo and Qin Chen and Yanpeng Cao and Xin Li},
title = {WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance},
year = {2020},
journal = {Computational Visual Media},
volume = {6},
number = {1},
pages = {65-78},
keywords = {video segmentation, water segmentation, appearance adaptation},
url = {https://www.sciopen.com/article/10.1007/s41095-020-0156-x},
doi = {10.1007/s41095-020-0156-x},
abstract = {We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide seg-mentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects such as water, whose appearance is non-uniform and changing dynamically, our pipeline can produce more reliable and accurate segmentation results than existing algorithms.}
}