温州医科大学图书馆书目检索系统

Online Public Access Catalogue

题名/责任者:

Bridging the Semantic Gap in Image and Video Analysis edited by Halina Kwaśnicka, Lakhmi C. Jain.

版本说明:

1st ed. 2018.

出版发行项:

Cham : Springer International Publishing : Imprint: Springer, 2018.

ISBN:

9783319738918

其它标准号:

10.1007/978-3-319-73891-8

载体形态项:

X, 163 p. 59 illus., 48 illus. in color. online resource.

主文献:

Springer eBooks

其他载体形态:

Printed edition: 9783319738901

其他载体形态:

Printed edition: 9783319738925

其他载体形态:

Printed edition: 9783030088798

丛编说明:

Intelligent Systems Reference Library, 1868-4394 ; 145

丛编统一题名:

Intelligent Systems Reference Library, 145

附加个人名称:

Kwaśnicka, Halina. editor.

附加个人名称:

Jain, Lakhmi C. editor.

附加团体名称:

SpringerLink (Online service)

论题主题:

Computational intelligence.

论题主题:

Semantics.

论题主题:

Artificial intelligence.

论题主题:

Signal processing.

论题主题:

Image processing.

论题主题:

Speech processing systems.

论题主题:

Optical data processing.

论题主题:

Computational Intelligence.

论题主题:

Semantics.

论题主题:

Artificial Intelligence.

论题主题:

Signal, Image and Speech Processing.

论题主题:

Image Processing and Computer Vision.

内容附注:

Semantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning – a New Era in Bridging the Semantic Gap.

摘要附注:

This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

电子资源:

https://doi.org/10.1007/978-3-319-73891-8

豆瓣简介：