Advances in Spatio-Temporal Segmentation of Visual Data (Hardcover, 1st ed. 2020)

This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal video segmentation and interpretation, online fuzzy clustering of data streams, and fuzzy systems for information retrieval. The main focus is on the spatio-temporal segmentation of visual information. Sets of meaningful and manageable image or video parts, defined by visual interest or attention to higher-level semantic issues, are often vital to the efficient and effective processing and interpretation of viewable information. Developing robust methods for spatial and temporal partition represents a key challenge in computer vision and computational intelligence as a whole. This book is intended for students and researchers in the fields of machine learning and artificial intelligence, especially those whose work involves image processing and recognition, video parsing, and content-based image/video retrieval.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 876
Release date:	2020
First published:	2020
Editors:	Vladimir Mashtalir • Igor Ruban • Vitaly Levashenko
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	274
Edition:	1st ed. 2020
ISBN-13:	978-3-03-035479-4
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	3-03-035479-2
Barcode:	9783030354794

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