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Advances in spatio-temporal segmenta...

Mashtalir, Vladimir.

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Advances in spatio-temporal segmentation of visual data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in spatio-temporal segmentation of visual data/ edited by Vladimir Mashtalir, igor Ruban, Vitaly Levashenko.
其他作者:	Mashtalir, Vladimir.
出版者:	Cham :Springer international Publishing : : 2020.,
面頁冊數:	ix, 274 p. :ill., digital ;24 cm.
內容註:	Adaptive Edge Detection Models and Algorithms -- Swarm Methods of image Segmentation -- Spatio-temporal Data interpretation Based on Perceptional Model -- Spatio-Temporal Video Segmentation.
Contained By:	Springer eBooks
標題:	Image segmentation. -
電子資源:	https://doi.org/10.1007/978-3-030-35480-0
ISBN:	9783030354800

Advances in spatio-temporal segmentation of visual data
Advances in spatio-temporal segmentation of visual data[electronic resource] /edited by Vladimir Mashtalir, igor Ruban, Vitaly Levashenko. - Cham :Springer international Publishing :2020. - ix, 274 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8761860-949X ;. - Studies in computational intelligence ;v.876..

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.

ISBN: 9783030354800

Standard No.: 10.1007/978-3-030-35480-0doiSubjects--Topical Terms:

2132796
Image segmentation.

LC Class. No.: TA1638.4 / .A383 2020

Dewey Class. No.: 006.6

Advances in spatio-temporal segmentation of visual data
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520 $a 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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