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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 and innovations in software design have placed the tools needed to realize this vision in the hands of the systematics community, not several years hence, but now. And not just for DNA barcodes or other molecular data, but for digital images of organisms, digital sounds, digitized chemical data - essentially any type of digital data.
Based on evidence accumulated over the last decade and written by applied researchers, Automated Taxon Identification in Systematics explores contemporary applications of quantitative approaches to the problem of taxon recognition. The book begins by reviewing the current state of systematics and placing automated taxon identification in the context of contemporary trends, needs, and opportunities. The chapters present and evaluate different aspects of current automated system designs. They then provide descriptions of case studies in which different theoretical and practical aspects of the overall group-identification problem are identified, analyzed, and discussed.
A recurring theme through the chapters is the relationship between taxonomic identification, automated group identification, and morphometrics. This collection provides a bridge between these communities and between them and the wider world of applied taxonomy. The only book-length treatment that explores automated group identification in systematic context, this text also includes introductions to basic aspects of the fields of contemporary artificial intelligence and mathematical group recognition for the entire biological community.
| Introduction | p. 1 |
| Digital Innovation and Taxonomy's Finest Hour | p. 9 |
| Natural Object Categorization: Man versus Machine | p. 25 |
| Neural Networks in Brief | p. 47 |
| Morphometrics and Computed Homology: An Old Theme Revisited | p. 69 |
| The Automated Identification of Taxa: Concepts and Applications | p. 83 |
| DAISY: A Practical Computer-Based Tool for Semi-Automated Species Identification | p. 101 |
| Automated Extraction and Analysis of Morphological Features for Species Identification | p. 115 |
| Introducing SPIDA-Web: Wavelets, Neural Networks and Internet Accessibility in an Image-Based Automated Identification System | p. 131 |
| Automated Tools for the Identification of Taxa from Morphological Data: Face Recognition in Wasps | p. 153 |
| Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report | p. 189 |
| Plant Identification from Characters and Measurements Using Artificial Neural Networks | p. 207 |
| Spot the Penguin: Can Reliable Taxonomic Identifications Be Made Using Isolated Foot Bones? | p. 225 |
| A New Semi-Automatic Morphometric Protocol for Conodonts and a Preliminary Taxonomic Application | p. 239 |
| Decision Trees: A Machine-Learning Method for Characterizing Morphological Patterns Resulting from Ecological Adaptation | p. 261 |
| Data Integration and Multifactorial Analyses: The Yeasts and the BioloMICS Software as a Case Study | p. 277 |
| Automatic Measurement of Honeybee Wings | p. 289 |
| Good Performers Know Their Audience! Identification and Characterization of Pitch Contours in Infant- and Foreigner-Directed Speech | p. 299 |
| Appendix | p. 311 |
| Subject Index | p. 329 |
| Taxon Index | p. 337 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780849382055
ISBN-10: 084938205X
Series: Systematics Association Special Volumes
Audience:
Tertiary; University or College
Format:
Hardcover
Language:
English
Number Of Pages: 368
Published: 1st June 2007
Dimensions (cm): 25.4 x 17.8
x 2.2
Weight (kg): 0.794
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