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30 December 2003 Automatic screening and multifocus fusion methods for diatom identification
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The first part of this paper presents a new method for the classification and screening of diatoms in images taken from water samples. The technique can be split into three main stages: segmentation, object feature extraction and classification. The segmentation part consists of two modified thresholding and contour tracing techniques in order to detect the majority of objects present at the sample. From the segmented objects, several features have been extracted and analyzed. For the classification, a diatom training set was considered and the centroids, means and variances of four different classes were found. For the identification process diatoms were classified according with their Mahalanobis distance. The results show the method ability to select at least 80% of usable diatoms from images contaminated with debris. Secondly, full automation of the diatom classification is achieved when multi-focal microscopy is utilized for water sample acquisition. In this case, a necessary preprocessing step is image fusion. A novel wavelet-based fusion method proposed here returns a sharp image that can be directly used for segmentation. For a better understanding of the diatom shape, a 2.5D reconstruction is given.
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Manuel Forero, Filip Sroubek, Jan Flusser, Rafael Redondo, and Gabriel Cristobal "Automatic screening and multifocus fusion methods for diatom identification", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003);


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