Summary
This chapter looked at the use of automatic techniques for the analysis of microscopic objects. It showed all stages of the analysis, from the initial hardware set-up, through the focusing procedure and object detection, to the final classification of the objects. It presented a case study of a pollen classification system that can be trained on a sample of images of pollen grains. The system was based on a flexible neural network architecture: such networks trained on individual species can be combined to produce a discriminator for the set of species. The chapter presented results based on a trial with a large number of images.
Experience in the face recognition community has shown that, for small data sets, it is easy to get good results; problems arise when attempts are made to scale up techniques to realistic amounts of data. Comparing the results of different techniques is also problematic. Large public datasets are required along with protocols for testing the recognition systems.
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France, I., Duller, A.W.G., Duller, G.A.T. (2005). Software Aspects of Automated Recognition of Particles: The Example of Pollen. 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_13
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DOI: https://doi.org/10.1007/1-4020-2122-4_13
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-2061-2
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