A METHOD OF IMAGE SEGMENTATION WITH THE HELP OF AREAS GROWINAND MULTISCALE ANALISYS

Abstract

In this article we analyzed advantages and disadvantages of existing methods of image segmentation. The development of an original algorithm of segmentation which uses the method of areas growing and multi scale analysis is presented. The capabilities of this method in different images segmentation are researched.

Full Text

Segmentation means selection of homogeneous fields in original digital images. It is one of the most important problems in modern systems of computer vision, which are used in many scientific and industrial spheres: medicine, metallography, air-photography, robotics, safety systems and others. It is at this stage of processing that conversion of an image from a set of pixels into a set of segments suitable for further recognition of scene objects takes place. Nowadays there is a number of formalized methods of segmentation [1], which can be divided into two groups according to the basic principle of working: Mathematics, mechanics, computer science 112 – methods which initially select area boundaries (contours) as the drops of some feature of an image; – methods which select segments having a homogeneous feature. The first group includes methods which calculate the first and the second derivatives of the image function with the help of different masks (Roberts operator, Previtt operator, Laplace operator, Marr-Hildreth operator, etc.), supplemented with methods of contour binding (local binding, Hough’s transformation, analysis with the help of graph theory). The main problem of these contours determination methods is that the derivable borders of an object are disconnected. It is understandable because the basic algorithms of segmentation are not set up to produce connected closed contours, and methods of binding are a superstructure for these algorithms and can solve this problem only at the expense of great computational burden.