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.
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Article type
Year
Open Access
Research Article
Issue
Computational Visual Media 2020, 6 (1): 65-78
Published: 23 March 2020
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