Advances in Spatio-Temporal Segmentation of Visual Data

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. 

Inhaltsverzeichnis

Adaptive Edge Detection Models and Algorithms.- Swarm Methods of Image Segmentation.- Spatio-temporal Data Interpretation Based on Perceptional Model.- Spatio-Temporal Video Segmentation.

Produktdetails

• Mitarbeit: Vitaly Levashenko (Herausgeber), Vladimir Mashtalir (Herausgeber), Igo Ruban (Herausgeber), Igor Ruban (Herausgeber)
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 30.01.2021
• EAN: 9783030354824
• ISBN: 978-3-0-3035482-4
• Seiten: 274
• Abmessung: 155 mm x 15 mm x 235 mm
• Illustration: IX, 274 p.
• Serie: Studies in Computational Intelligence
• Thema: Naturwissenschaften, Medizin, Informatik, Technik > Technik > Allgemeines, Lexika