МЕТОД СИНТЕЗА МНОГОКОМПОНЕНТНОЙ МОДЕЛИ АТРИБУТИВНЫХ ПРИЗНАКОВ ОБЪЕКТОВ

The subject matter of the article are the processes of forming of objects’ attribute features analytical descriptions for solving applied problems of statistical recognition of objects’ images on multi-channel images. The goal is to develop a multicomponent mathematical model for representing statistical information about the summation of geometric, colour and structural parameters of observational objects. The tasks to be solved are: to formalize the procedure of statistical image segmentation in conditions of incomplete a priori information about objects classes and unknown distribution densities of classification characteristics; to build effective algorithms for detection and linking contour points; to choose a universal mathematical model for describing the geometric shape of both the object and its structural components and to develop a robust method for estimating the model parameters. The methods used are: statistical methods of pattern recognition, methods of probability theory and mathematical statistics, methods of contour analysis, numerical methods for conditional optimization. The following results were obtained. The method of multicomponent model synthesis for describing colour, geometric and structural attributes of object images on multichannel images is proposed. In the model terms, the object is represented by a hierarchical set of nested contours, for the selection of which information about the colour characteristics of statistically homogeneous regions of the image is used. Methods for detecting and linking contour points have been developed, which make it possible to obtain the coordinates of the boundaries circular sweep for both convex and concave geometric objects. As a universal basis for describing the model components, the Johnson SB distribution is adopted, which allows us to describe practically any unimodal and wide class of bimodal distributions. A method for Johnson distribution parameters’ estimation from sample data, based on the method of moments and using optimization procedures for a non-linear objective function with constraints is given. Conclusions. The scientific novelty of the results obtained is as follows: the methods for describing the objects’ images in the form of a combination of several bright-geometric elements and structural connections between them have been further developed, which makes it possible to comprehensively take into account the attribute features of objects in the procedures for analyzing and interpreting images, automatically detecting and locating objects with specified characteristics

An improved spatial contour tree constructed method

Contours are important data to delineate the landform on a map. A contour tree provides an object-oriented description of landforms and can be used to enrich the topological information. The traditional contour tree is used to store topological relationships between contours in a hierarchical structure and allows for the identification of eminences and depressions as sets of nested contours. This research proposes an improved contour tree so-called spatial contour tree that contains not only the topological but also the geometric information. It can be regarded as a terrain skeleton in 3-dimention, and it is established based on the spatial nodes of contours which have the latitude, longitude and elevation information. The spatial contour tree is built by connecting spatial nodes from low to high elevation for a positive landform, and from high to low elevation for a negative landform to form a hierarchical structure. The connection between two spatial nodes can provide the real distance and direction as a Euclidean vector in 3-dimention. In this paper, the construction method is tested in the experiment, and the results are discussed. The proposed hierarchical structure is in 3-demintion and can show the skeleton inside a terrain. The structure, where all nodes have geo-information, can be used to distinguish different landforms and applied for contour generalization with consideration of geographic characteristics.