Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
ATT Laboratories Archive, Olivetti Research Laboratory Database of Faces, http://www.uk.research.att.com/facedatabase.html.
Bé A.W.H., Harrison S.M. and Lott L. 1973. Orbulina universa ďOrbigny in the Indian Ocean. Micropaleontology 19: 150–192.
Beaufort L., de Garidel-Thoron T., Mix A.C. and Pisias N.G. 2001. ENSO-like forcing on oceanic primary production during the Late Pleistocene. Science 293: 2440–2444.
Belgrano A., Malmgren B.A. and Lindahl O. 2001. Application of artificial neural networks (ANN) to primary production time-series data. J. Plankton Res. 23: 651–658.
Belyea P.R. and Thunell R.C. 1984. Fourier shape analysis and planktonic foraminiferal evolution: The Neogloboquadrina — Pulleniatina lineages. J. Paleontol. 58: 1026–1040.
Bishop C.M. 1995. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 482 pp.
Bollmann J. 1997. Morphology and biogeography of Gephyrocapsa coccoliths in Holocene sediments. Mar. Micropaleontol. 29: 319–350.
Bollmann J., Brabec B., Cortés M.Y. and Geisen M. 1999. Determination of absolute coccolith abundances in deep-sea sediments by spiking with microbeads and spraying (SMS method). Mar. Micropalaeontol. 38: 29–38.
Bollmann J., Cortés M.Y., Lenz B., Llinas O., Müller T. and Reuter R. 2000. Distribution of living coccolithophores North of the Canary Islands: Vertical, seasonal and interannual variations. EOS Transactions AGU 81 48: F204.
Bollmann J., Cortés M.Y., Haidar A.T., Brabec B., Close A., Hofmann R., Palma S., Tupas L. and Thierstein H.R. 2002a. Techniques for quantitative analyses of calcareous marine phytoplankton. Mar. Mircropaleontol. 44: 163–185.
Bollmann J., Henderiks J. and Brabec B. 2002b. Global Calibration of Gephyrocapsa coccolith abundance in Holocene sediments for paleotemperature assessment. Paleoceanography 17: 7-1 to 7-9.
Bown P.R. and Young J.R. 1998. Techniques. In: Bown P.R. (ed.), Calcareous Nannofossil Biostratigraphy. British Micropalaeontological Society Publication Series, pp. 16–28.
Brechner S. 2000. Automatic coccolith classification and extraction of morphological features in SEM images. Selected Readings in Vision and Graphics 5, Hartung-Gorre Verlag, Konstanz, 205 pp.
Burke C.D., Full W.E. and Gernant R.E. 1987. Recognition of fossil freshwater ostracodes — fourier shape analysis. Lethaia 20: 307–314.
CLIMAP Project Members, 1976. The surface of the ice-age earth. Science 191: 1131–1137.
COSOD II Working Group Members, 1986. Report of the Second Conference on Scientific Ocean Drilling (COSOD II). Strasbourg. European Science Foundation.
Culverhouse P.F., Simpson R.G., Ellis R., Lindley J.A., Williams R., Parisini T., Beguera B., Bravo I., Zoppoli R., Earnshaw G., McCall H. and Smith G. 1996. Automatic classification of field-collected dinoflagellates by artificial neural network. Mar. Ecol. Prog. Ser. 139: 281–287.
Dollfus D. 1997. Reconnaissance de formes naturelles par des réseaux de neurones artificiels: application au nannoplancton calcaire. Ph.D. thesis, CEREGE, Université ďAix-Marseilles.
Dollfus D. and Beaufort L. 1999. Fat neural network for recognition of position-normalised objects. Neural Networks 12: 553–560.
du Buf H. and Bayer M.M. 2002. Automatic diatom identification. World Scientific, Series in machine perception and artificial intelligence 51, 316 pp.
Ehrlich R. and Weinberg B. 1970. An exact method for characterization of grain shape. J. Sed. Petrol. 40: 205–212.
Felix D.W. 1969. An inexpensive recording settling tube for analysis of sands. J. Sed. Petrol. 39: 777–780.
France I., Duller A.W.G., Duller G.A.T. and Lamb H.F. 2000. A new approach to automated pollen analysis. Quat. Sci. Rev. 19: 537–546.
Fueten F. 1997. A computer-controlled rotating polarizer stage for the petrographic microscope. Comp. Geosci. 23: 203–208.
Garratt J. and Swan A. 1992. Morphological data from coccolith images. In: Hamrsmid B. and Young J.R. (eds), Nannoplankton Research, Proceedings of the Fourth INA Conference, Prague 1991 Volume 1 Hodonin Prague, pp. 11–34.
Healy-Williams N. 1983. Fourier shape analysis of Globorotalia truncatulinoides from Late Quaternary sediments in the southern Indian Ocean. Mar. Micropaleontol. 8: 1–15.
Healy-Williams N. 1984. Quantitative image analysis: Application to planktonic foraminiferal paleoecology and evolution. Geobios Mémoire Spécial 8: 425–432.
Hecht A.D. 1976. An ecologic model for test size variation in recent planktonic foraminifera: applications to the fossil record. J. Foraminiferal Res. 6: 295–311.
Hills S. 1988. Outline extraction of microfossils in reflected light images. Comp. Geosci. 14: 481–488.
Imbrie J. and Kipp N. 1971. A new micropaleontological method for quantitative paleoclimatology: Application to a Late Pleistocene Caribbean Core. In: Turekian K.K. (ed.), The Late Cenozoic Glacial Ages. Yale Univ. Press, New Haven, Connect., pp. 71–181.
Knappertsbusch M. 2000. Morphologic evolution of the coccolithophorid C. leptoporus from the Early Miocene to Recent. J. Paleontol. 74: 712–730.
Knappertsbusch M., Cortés M.Y. and Thierstein H.R. 1997. Morphologic variability of the coccolithophorid Calcidicus leptoporus in the plankton, surface sediments and from the Early Pleistocene. Mar. Micropaleontol. 30: 293–317.
Kennett J.P. 1968. Globorotalia truncatulinoides as a Paleo-oceanographic Index. Science 159: 1461–1463.
Lazarus D., Hilbrecht H., Spencer-Cervato C. and Thierstein H.R. 1995. Sympatric speciation and phyletic change in Globorotalia truncatulinoides. Paleobiology 21: 28–51.
Lawrence S., Giles C.L., Tsoi A.C. and Black A.D. 1997. Face recognition: A convolutional neural network approach. IEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition 8: 89–113.
LeCun Y. 1987. Modèles connexionnistes de ľapprentissage. Ph.D. thesis, Université Pierre et Marie Curie, Paris, France.
LeCun Y., Boser B., Denker J.S., Henderson R.E., Howard W., Hubbard W. and Jackel L.D. 1990. Handwritten digit recognition with a back-propagation network. In: Touretzky D.S. (ed.), Advances in Neural Information Processing Systems. Morgan Kaufmann, pp. 396–404.
LeCun Y. and Bengio Y. 1995. Convolutional networks for images speech and time-series. In: Arbib M.A. (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, Massachusetts, pp. 255–258.
Lipps J. 1993. Fossil Prokaryotes and Protists. Blackwell Scientific Publications, Boston, 342 pp.
Malmgren B.A. and Nordlund U. 1997. Application of artificial neural networks to paleoceanographic data. Palaeogeogr. Palaeoclim. Palaeoecol. 136: 359–373.
Malmgren B.A. and Winter A. 1999. Climate zonation in Puerto Rico based on principal components analysis and an artificial neural network. J. Climate 12: 977–985.
Ratmeyer V. and Wefer R. 1996. A high resolution camera system (ParCa) for imaging particles in the ocean: System design and results from profiles and a three-month deployment. J. Mar. Res. 54: 589–603.
Ripley B.D. 1996. Pattern Recognition and Neural NetwSorks. Cambridge University Press, Cambridge, 403 pp.
Schalkoff R.J. 1997. Artificial Neural Networks. MIT Press and The McGraw-Hill Companies Inc., New York, 421 pp.
Schmidt D.N. 2002. Size variability in planktic foraminifera. Ph.D. thesis, ETH No. 14578, 121 pp.
Schmidt D.N., Renaud S. and Bollmann J. 2003. Response of planktic foraminiferal size to late Quaternary climate change. Paleoceanography 18: 17–1 to 17–12.
Schwarcz H.P. and Shane K.C. 1969. Measurement of particle shape by Fourier analysis. Sedimentology 13: 213–231.
Swaby P. 1990. Integrating artificial intelligence and graphics in a tool for microfossil identification for use in the petroleum industry. Proceedings of the 2nd Annual Conference on Innovative Applications of Artificial Intelligence, Washington, 203–218 pp.
Weiss S.M. and Kulikowski C.A. 1991. Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning and expert systems. Morgan Kaufmann Publishers Inc, San Francisco, California, 223 pp.
Westbroek P., Brown C.W., Van Bleijswijk J., Brownlee C., Grummer G.J., Conte M., Egge J., Fernandez E., Jordan R., Knappertsbusch M., Stefels J., Verdhuis M., Van der Wal P. and Young J.R. 1993. A model system approach to biological climate forcing. The example of Emiliania huxleyi. Glob. Planet. Change 8: 1–20.
Young J.R., Kucera M. and Chung H.-W. 1996. Automated biometrics on captured light microscope images of Emiliania huxleyi. In: Moguilevsky A. and Whatley R. (eds), Microfossils and Oceanic environments. Aberystwyth Press, Aberystwyth, pp. 261–280.
Yu S., Saint-Marc P., Thonnat M. and Berthold M. 1996. Feasibility study of automatic identification of planktic foraminifera by computer vision. J. Foraminiferal Res. 26: 113–123.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2005 Springer Science + Business Media, Inc.
About this chapter
Cite this chapter
Bollmann, J. et al. (2005). Automated Particle Analysis: Calcareous Microfossils. In: Francus, P. (eds) Image Analysis, Sediments and Paleoenvironments. Developments in Paleoenvironmental Research, vol 7. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2122-4_12
Download citation
DOI: https://doi.org/10.1007/1-4020-2122-4_12
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-2061-2
Online ISBN: 978-1-4020-2122-0
eBook Packages: Springer Book Archive