Advances in Spatio-Temporal Segmentation of Visual Data
(Sprache: Englisch)
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...
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Klappentext zu „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 zu „Advances in Spatio-Temporal Segmentation of Visual Data “
Adaptive Edge Detection Models and Algorithms.- Swarm Methods of Image Segmentation.- Spatio-temporal Data Interpretation Based on Perceptional Model.- Spatio-Temporal Video Segmentation.
Bibliographische Angaben
- 2020, 1st ed. 2020, IX, 274 Seiten, Masse: 15,8 x 24,3 cm, Gebunden, Englisch
- Herausgegeben: Vladimir Mashtalir, Igor Ruban, Vitaly Levashenko
- Verlag: Springer, Berlin
- ISBN-10: 3030354792
- ISBN-13: 9783030354794
Sprache:
Englisch
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