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Computational botany: methods for automated species identification
This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant spe...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
Springer
2017
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-662-53745-9 http://cds.cern.ch/record/2240528 |
_version_ | 1780953070856830976 |
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author | Remagnino, Paolo Mayo, Simon Wilkin, Paul Cope, James Kirkup, Don |
author_facet | Remagnino, Paolo Mayo, Simon Wilkin, Paul Cope, James Kirkup, Don |
author_sort | Remagnino, Paolo |
collection | CERN |
description | This book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification. |
id | cern-2240528 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2017 |
publisher | Springer |
record_format | invenio |
spelling | cern-22405282021-04-21T19:23:49Zdoi:10.1007/978-3-662-53745-9http://cds.cern.ch/record/2240528engRemagnino, PaoloMayo, SimonWilkin, PaulCope, JamesKirkup, DonComputational botany: methods for automated species identificationEngineeringThis book discusses innovative methods for mining information from images of plants, especially leaves, and highlights the diagnostic features that can be implemented in fully automatic systems for identifying plant species. Adopting a multidisciplinary approach, it explores the problem of plant species identification, covering both the concepts of taxonomy and morphology. It then provides an overview of morphometrics, including the historical background and the main steps in the morphometric analysis of leaves together with a number of applications. The core of the book focuses on novel diagnostic methods for plant species identification developed from a computer scientist’s perspective. It then concludes with a chapter on the characterization of botanists' visions, which highlights important cognitive aspects that can be implemented in a computer system to more accurately replicate the human expert’s fixation process. The book not only represents an authoritative guide to advanced computational tools for plant identification, but provides experts in botany, computer science and pattern recognition with new ideas and challenges. As such it is expected to foster both closer collaborations and further technological developments in the emerging field of automatic plant identification.Springeroai:cds.cern.ch:22405282017 |
spellingShingle | Engineering Remagnino, Paolo Mayo, Simon Wilkin, Paul Cope, James Kirkup, Don Computational botany: methods for automated species identification |
title | Computational botany: methods for automated species identification |
title_full | Computational botany: methods for automated species identification |
title_fullStr | Computational botany: methods for automated species identification |
title_full_unstemmed | Computational botany: methods for automated species identification |
title_short | Computational botany: methods for automated species identification |
title_sort | computational botany: methods for automated species identification |
topic | Engineering |
url | https://dx.doi.org/10.1007/978-3-662-53745-9 http://cds.cern.ch/record/2240528 |
work_keys_str_mv | AT remagninopaolo computationalbotanymethodsforautomatedspeciesidentification AT mayosimon computationalbotanymethodsforautomatedspeciesidentification AT wilkinpaul computationalbotanymethodsforautomatedspeciesidentification AT copejames computationalbotanymethodsforautomatedspeciesidentification AT kirkupdon computationalbotanymethodsforautomatedspeciesidentification |