Title
WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance
Document Type
Article
Publication Date
3-1-2020
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 segmentation, 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.
Publication Source (Journal or Book title)
Computational Visual Media
First Page
65
Last Page
78
Recommended Citation
Liang, Y., Jafari, N., Luo, X., Chen, Q., Cao, Y., & Li, X. (2020). WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance. Computational Visual Media, 6 (1), 65-78. https://doi.org/10.1007/s41095-020-0156-x