Method of extracting contours of objects in images using a fuzzy model

The article is devoted to methods and algorithms for constructing a fuzzy model for selecting contour pixels. There are an overview of expert approaches to constructing fuzzy models for selecting contour pixels and noted advantages and disadvantages. To automate the construction of fuzzy models for selecting contour pixels, a method for developing a composition of fuzzy production rules is proposed, based on Tsukamoto’s fuzzy inference and the analysis of brightness gradients of eight neighboring pixels in the direction of the pixel under consideration. To build the model, a single grayscale image with normalized brightness values is used. To decide whether a pixel belongs to a contour, eight linguistic variables of the "brightness gradient" model are introduced for each pixel surrounding the pixel in question, and a linguistic variable "contour belonging" is also introduced. For each linguistic variable introduced fuzzy sets with parametric membership functions. To construct the structure and composition of a fuzzy model, proposed an approach based on the correlation of a halftone and its black-and-white equivalent. To optimize the parameters of the membership functions used a data set which is formed on the basis of the original halftone image and the contours of objects applied to it. The generated data set is used by a genetic optimization algorithm. For each chromosome of the population, the fitness function is calculated using a fuzzy model with the corresponding parameter values. Given examples of application of the developed fuzzy model to other images.