Automated Taxon Identification in Systematics
Theory, Approaches and Applications
Published on 23. July 2007
368 pages
978-1-4200-0807-4 (ISBN)
Description
The automated identification of biological objects or groups has been a dream among taxonomists and systematists for centuries. However, progress in designing and implementing practical systems for fully automated taxon identification has been frustratingly slow. Regardless, the dream has never died. Recent developments in computer architectures an
More details
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Systematic biologists, evolutionary biologists, paleontologists, botanists, plant scientists, zoologists, ecologists, conservation biologists, computer scientists, mathematicians, and statisticians.
Illustrations
143 s/w Abbildungen, 10 farbige Abbildungen, 46 s/w Tabellen, 60 s/w Photographien bzw. Rasterbilder
143 b/w images, 10 color images, 46 tables and 60 halftones
File size
17,59 MB
ISBN-13
978-1-4200-0807-4 (9781420008074)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
10/2019
1st Edition
CRC Press
€94.00
Shipment within 15-20 days
Book
07/2007
1st Edition
CRC Press
€235.44
Article not available at the moment
Content
Introduction. The Need for Automated Approaches to Species Identification. Is Automated Species Identification Feasible? Natural Object Recognition: Machines Vs. Humans. Homology and Morphometrics: An Old Theme Revisited. Plastic Self-Organizing Maps. Decision Trees: A Machine-learning Methodology to Analyze the Relationship between Skeletal Morphology and Ecological Adaptations. DAISY: A Practical Computer Based Tool for Semi-Automated Species Identification. Introducing SPIDA-web: An Automated Identification System for Biological Species. Automated Extraction and Analysis of Morphological Features for Species Identification. Pattern Recognition for Ecological Science and Environmental Monitoring. Identification of Botanical Taxa using Artificial Neural Networks. Use of Neural Nets in Identification of Spheniscid Species. Drawing the Line: the Differentiation Between Morphological Plasticity and Interspecific Variation. Summary and Prospectus.
