DEVELOPMENT OF METHODS FOR DETERMINING THE CONTOURS OF OBJECTS FOR A COMPLEX STRUCTURED COLOR IMAGE BASED ON THE ANT COLONY OPTIMIZATION ALGORITHM

A method for determining the contours of objects on complexly structured color images based on the ant colony optimization algorithm is proposed. The method for determining the contours of objects of interest in complexly structured color images based on the ant colony optimization algorithm, unlike the known ones, provides for the following. Color channels are highlighted. In each color channel, a brightness channel is allocated. The contours of objects of interest are determined by the method based on the ant colony optimization algorithm. At the end, the transition back to the original color model (the combination of color channels) is carried out. A typical complex structured color image is processed to determine the contours of objects using the ant colony optimization algorithm. The image is presented in the RGB color space. It is established that objects of interest can be determined on the resulting image. At the same time, the presence of a large number of "garbage" objects on the resulting image is noted. This is a disadvantage of the developed method. A visual comparison of the application of the developed method and the known methods for determining the contours of objects is carried out. It is established that the developed method improves the accuracy of determining the contours of objects. Errors of the first and second kind are chosen as quantitative indicators of the accuracy of determining the contours of objects in a typical complex structured color image. Errors of the first and second kind are determined by the criterion of maximum likelihood, which follows from the generalized criterion of minimum average risk. The errors of the first and second kind are estimated when determining the contours of objects in a typical complex structured color image using known methods and the developed method. The well-known methods are the Canny, k-means (k=2), k-means (k=3), Random forest methods. It is established that when using the developed method based on the ant colony optimization algorithm, the errors in determining the contours of objects are reduced on average by 5–13 %