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  • Title: [Computerized image analysis in recognition and classification of aeroallergens].
    Author: Wawrzyniak ZM, Rapiejko P, Jachowicz RS, Jurkiewicz D.
    Journal: Pol Merkur Lekarski; 2005 Sep; 19(111):315-8. PubMed ID: 16358855.
    Abstract:
    UNLABELLED: In medical practice and research, it would be convenient to receive pollen identification and monitoring results in much shorter time than it comes from human identification. Image based analysis is one of the approaches to an automated identification scheme for pollens grain and pattern recognition on such images is widely used as a powerful tool. PURPOSE OF THE STUDY: The goal of such attempt is to provide accurate, fast recognition and classification and counting of pollen grains by computer system for monitoring. METHODS AND MATERIAL: The isolated pollen grain are objects extracted from microscopic image by CCD camera and PC computer under proper condition for further analysis. RESULTS: The algorithms are based on the knowledge from feature vector analysis of estimated parameters calculated from grain characteristics including morphological features, surface features and other applicable estimated characteristics. Segmentation algorithms specially tailored to pollen object characteristics provide exact descriptions of pollen characteristics (border and internal features) already used by human expert. The specific characteristics and its measures are statistically estimated for each object. Some low level statistics for estimated local and global measures of the features establish feature space. Some special care should be paid on choosing these feature and on constructing the feature space to optimize the number of subspaces for higher recognition rates in low-level classification for type differentiation of pollens grain. CONCLUSIONS: The results of estimated parameters of feature vector in low dimension space for some typical pollen types are presented as well as some effective and fast recognition results of performed experiments for different pollens. The findings show the evidence of using proper chosen estimators of tailored characteristics for good enough classification measures even for low dimensional classifiers for type differentiation of pollens grain.
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