Abstract
The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.
Highlights
In artificial intelligence systems, the segmentation and classification of objects on medical images are widely used Fourier descriptors.Analysis of existing approaches has shown that the known methods for determining the image descriptors and their segments such as: SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features), PCA-SIFT (PCA-Principal Component Analysis) etc. intended for use in intelligent systems and similar search similar images, but not in the classification systems
A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors
A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors was proposed
Summary
In artificial intelligence systems, the segmentation and classification of objects on medical images are widely used Fourier descriptors. Intended for use in intelligent systems and similar search similar images, but not in the classification systems. The implementation of these algorithms requires a large volume of samples [1]. With regard to medical images provide necessary training sample is not always possible.
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