Automatic diatom identification / editors, Hans du Buf, Micha M. Bayer.

Format:

Book

Contributor:

Du Buf, Hans.

Bayer, Micha M.

Series:

Series in machine perception and artificial intelligence ; v. 51.

Series in machine perception and artificial intelligence ; v. 51

Language:

English

Subjects (All):

Diatoms--Identification--Data processing.

Diatoms.

Pattern recognition systems.

Physical Description:

1 online resource (329 p.)

Edition:

1st ed.

Place of Publication:

River Edge, N.J. : World Scientific, c2002.

Language Note:

English

Summary:

This is the first book to deal with automatic diatom identification. It provides the necessary background information concerning diatom research, useful for both diatomists and non-diatomists. It deals with the development of electronic databases, image preprocessing, automatic contour extraction, the application of existing contour and ornamentation features and the development of new ones, as well as the application of different classifiers (neural networks, decision trees, etc.). These are tested using two image sets: (i) a very difficult set of <i>Sellaphora pupula</i> with 6 demes and 12

Contents:

Contents ; Preface ; Acknowledgments ; Authors' Affiliations ; Chapter 1 Introduction to ADIAC and This Book ; 1 History and structure of the project ; 2 Research in biology and diatoms ; 3 The chapters ; References ; Chapter 2 Diatoms: Organism and Image ; 1 General introduction

2 Introduction to the diatom cell 3 Classifying and identifying diatoms ; 4 Complications ; 5 Final comments ; References ; Chapter 3 Diatom Applications ; 1 Introduction ; 2 Forensic science: the diagnosis of drowning ; 3 Environmental monitoring ; 4 Palaeoecological applications ; References

Chapter 4 ADIAC Imaging Techniques and Databases 1 Microscopy and image acquisition ; 2 Image Databases ; 3 Pandora taxonomic database ; 4 WWW database ; References ; Chapter 5 Human Error and Quality Assurance in Diatom Analysis ; 1 Introduction ; 2 Material and methods

3 Results 4 Discussion ; References ; Chapter 6 Contour Extraction ; 1 Introduction ; 2 Pre-segmentation ; 3 Contour extraction ; 4 Results ; 5 Conclusion ; References ; Chapter 7 Identification Using Classical and New Features in Combination with Decision Tree Ensembles

1 Introduction 2 Feature extraction ; 3 Classification by decision trees ; 4 Data sets used ; 5 Identification results ; 6 Conclusions ; References ; Chapter 8 Identification by Curvature of Convex and Concave Segments ; 1 Introduction ; 2 Feature extraction

3 Classifier and test sets

Notes:

Description based upon print version of record.

Includes bibliographical references.

ISBN:

9789812777867

9812777865

OCLC:

879023634