Computer vision based identification and mosaic of gramineous grass seeds / Xin Pan, Xuanhe Zhao, Weihong Yan, Jiangping Liu, Xiaoling Luo, Tana Wuyun.
Pan, Xin, author.; Zhao, Xuanhe, author.; Yan, Weihong, author.; Liu, Jiangping, author.; Luo, Xiaoling, author.; Wuyun, Tana, author.
2021
QK495.G74 P36 2021
Available Online

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Title Computer vision based identification and mosaic of gramineous grass seeds / Xin Pan, Xuanhe Zhao, Weihong Yan, Jiangping Liu, Xiaoling Luo, Tana Wuyun.
Author Pan, Xin, author.
ISBN 9789811635014 (electronic bk.)
9811635013 (electronic bk.)
9789811635007
9811635005
DOI https://doi.org/10.1007/978-981-16-3501-4
Published Singapore : Springer ; Beijing : China Agricultural Science and Technology Press, [2021]
Copyright ©2021
Language English
Description 1 online resource : illustrations (chiefly color)
Other Standard Identifiers 10.1007/978-981-16-3501-4 doi
Call Number QK495.G74 P36 2021
Dewey Decimal Classification 584/.9
Summary This book mainly deals with grassland digitalization and recognition through computer vision, which will make contributions to implement of grass auto recognition and data acquisition. Taking advantage of computer vision, it focuses on intrinsic feature extraction to realize the functions such as auto recognition of forage seeds and microscope images mosaic. The book presents a new approach for identification of grass seeds, with clear figures and detailed tables. It enlightens reader by solving the traditional problems of pratacultural science through the aid of computer science.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 27, 2021).
Added Author Zhao, Xuanhe, author.
Yan, Weihong, author.
Liu, Jiangping, author.
Luo, Xiaoling, author.
Wuyun, Tana, author.
Available in Other Form Print version: 9789811635007
Linked Resources Online Access
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Introduction
Forage Identification and Experimental Materials
Identification of Gramineous Grass Seeds Using Gabor and Locality Preserving Projections
Identification of Gramineous Grass Seeds Using Difference of Local Fractal Dimensions
Identification of Gramineous Grass Seeds Using Local Similarity Pattern and Linear Discriminant Analysis
Identification of Gramineous Grass Seeds Using Local Similarity Pattern and Gray Level Co-occurrence Matrix
Microscopic Image Mosaic of Gramineous Grass Seeds
Digital Information Platform of Grassland and Forage Based on Computer Vision.

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