Computer Vision based Identification and Mosaic of Gramineous Grass Seeds

Pan, Xin; Zhao, Xuanhe; Yan, Weihong; Liu, Jiangping; Luo, Xiaoling; Wuyun, Tana

ISBN 10: 9811635005 / ISBN 13: 9789811635007

Published by Springer, 2021

About this Item

In.

Seller Inventory # ria9789811635007_new

Bibliographic Details

• Title: Computer Vision based Identification and Mosaic of Gramineous Grass Seeds
• Publisher: Springer
• Publication Date: 2021
• Language: English
• ISBN 10: 9811635005
• ISBN 13: 9789811635007
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Author

Dr. Xin Pan is a Professor of College of Computer and Information Engineering, Inner Mongolia Agricultural University. She received her Ph.D. in signal and information processing from Beijing Jiaotong University in 2009. Then she carried out her postdoctoral researches in Grassland Research Institute of Chinese Academy of Agricultural Sciences from 2010-2014. Dr. Pan Xin’s research interest is image processing and pattern recognition. Her work focuses on solving the traditional problems of pratacultural science by the means of computer vision. She has published more than 30 papers, and is a member of professional organizations of China Institute of Communications and Chinese Grassland Society. Her research is an initial effort in the research of grassland digitization based on computer vision, especially focus on identification and mosaic of gramineous grass seeds, which provides a new approach for automated data acquisition of grassland.