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Record Nr. |
UNINA9910366616003321 |
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Titolo |
Advances in Spatio-Temporal Segmentation of Visual Data / / edited by Vladimir Mashtalir, Igor Ruban, Vitaly Levashenko |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (279 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 876 |
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Disciplina |
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Soggetti |
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Engineering mathematics |
Optical data processing |
Computational intelligence |
Engineering Mathematics |
Image Processing and Computer Vision |
Computational Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Adaptive Edge Detection Models and Algorithms -- Swarm Methods of Image Segmentation -- Spatio-temporal Data Interpretation Based on Perceptional Model -- Spatio-Temporal Video Segmentation. |
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Sommario/riassunto |
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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 |
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intelligence, especially those whose work involves image processing and recognition, video parsing, and content-based image/video retrieval. . |
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