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Advances in Spatio-Temporal Segmentation of Visual Data [electronic resource] / edited by Vladimir Mashtalir, Igor Ruban, Vitaly Levashenko.

Contributor(s): Mashtalir, Vladimir [editor.] | Ruban, Igor [editor.] | Levashenko, Vitaly [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 876Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: IX, 274 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030354800.Subject(s): Engineering mathematics | Computer vision | Computational intelligence | Engineering Mathematics | Computer Vision | Computational IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 620.00151 Online resources: Click here to access online
Contents:
Adaptive Edge Detection Models and Algorithms -- Swarm Methods of Image Segmentation -- Spatio-temporal Data Interpretation Based on Perceptional Model -- Spatio-Temporal Video Segmentation.
In: Springer Nature eBookSummary: 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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Adaptive Edge Detection Models and Algorithms -- Swarm Methods of Image Segmentation -- Spatio-temporal Data Interpretation Based on Perceptional Model -- Spatio-Temporal Video Segmentation.

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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