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Automatic recognition of biological particles in microscopic images

Ranzato, M. and Taylor, P. E. and House, J. M. and Flagan, R. C. and LeCun, Y. and Perona, P. (2007) Automatic recognition of biological particles in microscopic images. Pattern Recognition Letters, 28 (1). pp. 31-39. ISSN 0167-8655. doi:10.1016/j.patrec.2006.06.010.

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A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good.

Item Type:Article
Related URLs:
URLURL TypeDescription
Flagan, R. C.0000-0001-5690-770X
Perona, P.0000-0002-7583-5809
Additional Information:© 2006 Elsevier B.V. Received 6 September 2005, Revised 1 June 2006, Available online 2 August 2006.
Subject Keywords:Biological particles; Feature; Non-linearity; Recognition; Cells; Pollen; Mixture of Gaussians (MoG)
Issue or Number:1
Record Number:CaltechAUTHORS:20140730-101718040
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47604
Deposited By: Caroline Murphy
Deposited On:18 Aug 2014 19:01
Last Modified:10 Nov 2021 17:48

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