Automated Taxon Identification in Systematics
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Anterior Tentorial Pit
artificial
artificial intelligence methods
ATI
automated species recognition systems
biological pattern recognition
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Category=UN
Category=UNF
Category=UY
computational taxonomy
Cva
DA
DNA Barcoding
EA
ecological data analysis
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Front Wings
Image Analysis
Kreger Van Rij
machine learning classification
macleod
Mage
map
Morphometric Approaches
morphometric data processing
network
neural
Neural Net
norman
Partial Warps
principal
Procrustes Distance
Reference Vector
Selective Feeders
self-organizing
set
Shape Coordinates
Sift Descriptor
Species Identification
Stonefly Larvae
Taxonomic Impediment
Topotype Material
training
Training Set
Wing Image
Wing Measurements
Product details
- ISBN 9780849382055
- Weight: 793g
- Dimensions: 178 x 254mm
- Publication Date: 23 Jul 2007
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
Norman MacLeod
