WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

Autor:	Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	video segmentation water segmentation appearance adaptation Electronic computers. Computer science QA75.5-76.95
Zdroj:	Computational Visual Media, Vol 6, Iss 1, Pp 65-78 (2020)
Druh dokumentu:	article
ISSN:	2096-0433 2096-0662
DOI:	10.1007/s41095-020-0156-x
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8e01b3d7333241149d5ef8ef86831092 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ