This work deals with the design of a classification method for cells extracted from Indirect Immunofluorescence images. In particular, we propose to use a dense local descriptor invariant both to scale changes and to rotations in order to classify the six categories of staining patterns of the cells. The descriptor is able to give a compact and discriminative representation and combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag of Words is finally used to perform classification and experimental results show very good performance.
Biologically-inspired dense local descriptor for indirect immunofluorescence image classification
Gragnaniello D;
2014
Abstract
This work deals with the design of a classification method for cells extracted from Indirect Immunofluorescence images. In particular, we propose to use a dense local descriptor invariant both to scale changes and to rotations in order to classify the six categories of staining patterns of the cells. The descriptor is able to give a compact and discriminative representation and combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag of Words is finally used to perform classification and experimental results show very good performance.File in questo prodotto:
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