WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance
Yongqing Liang, Navid H. Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li · Computational Visual Media · 2020
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.