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Advances in Spatio-Temporal Segmentation of Visual Data (Hardcover, 1st ed. 2020): Vladimir Mashtalir, Igor Ruban, Vitaly... Advances in Spatio-Temporal Segmentation of Visual Data (Hardcover, 1st ed. 2020)
Vladimir Mashtalir, Igor Ruban, Vitaly Levashenko
R2,678 Discovery Miles 26 780 Ships in 18 - 22 working days

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.

Advances in Spatio-Temporal Segmentation of Visual Data (Paperback, 1st ed. 2020): Vladimir Mashtalir, Igor Ruban, Vitaly... Advances in Spatio-Temporal Segmentation of Visual Data (Paperback, 1st ed. 2020)
Vladimir Mashtalir, Igor Ruban, Vitaly Levashenko
R2,653 Discovery Miles 26 530 Ships in 18 - 22 working days

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.

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