Automated Taxon Identification in Systematics shows how automated identification can be applied to various organismal groups. It is the first text to address the interface between the species recognition question in the biological sciences and the class recognition problem in mathematics and statistics. It will be of interest to taxonomists as well as computational professionals who focus on the biological sciences. Featuring high profile contributors, the text presents current trends in quantitative approaches to group-recognition challenges, discusses the identification capabilities of current software systems, and evaluates applications of this technology to present and future problems.
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-OrganizingMaps. 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 AutomatedIdentification 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 usingArtificial 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.
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Natural History Museum, London, UK