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The image classifier consists of two interacting modules: feature extraction and linear classification. Feature extraction relies on evaluating spatial derivatives of integer or fractional order of the image, followed by non-linear transformations in the Fourier domain. Linear classification relies on multivariate statistical analysis of feature vectors. Training minimizes a loss function: the latter depends on the parameter n-tuple which controls feature extraction. Application: discrimination of bacterial spores among airborne particulate material.
This presentation is part of Contributed Presentation “CP4 - Contributed session 4”