Method for Recognition of Fuzzy 2 D Primitives via a Technology of Soft Computing

A method for recognition of fuzzy 2D image primitives based on a soft computing technology is presented. In this method, the descriptions of primitives are formed by a human expert in a quasi-natural language. The recognition methodology is suggested for the primitives represented in this form. This methodology is based on the theory of fuzzy sets and on the technology of test score semantics. The suggested methodology has been employed in a human-machine software system that is intended for recognition of ultrasonic images of a fetus in mother’s womb. Received July 30, 1999 INTRODUCTION One of the basic problems within the structural approach to image recognition consists in recognition of image primitives. The simplest 2D shapes that form an image structure are often used as such primitives. As a rule, these shapes are selected in the process of image recognition at the stage of segmentation. The efficiency of solving the recognition problem for the primitives largely determines the efficiency of the complete image recognition process and, therefore, the solution of this problem is often performed under strictest requirements. In most cases, the fuzzy nature of actual images primarily affects a shape fuzziness of 2D primitives in these images. Thus, a solution of the recognition problem for 2D primitives under conditions of their fuzziness is of particular interest. The quality of recognition of 2D primitives is largely determined by the efficiency of a form for their description. The principal criteria that determine this efficiency are given in [1]. The knowledge about the psychophysical features of human vision is more often employed for forming the effective descriptions of 2D shapes [2, 4]. In this case, the recognition system based on a human vision model is said to be constructed. There are the following principle features of 2D shape recognition by the human visual system: large informative significance of a curvature function for 2D shape contours, superiority of qualitative features over the quantitative ones, fuzziness of human thinking, and variatiability of significance of shape features in different cases [1-4]. The behavior of the curvature function, its shape, and extreme points represent the most informative data for the human vision system. In most cases, the attempts of including the above features in the processes of 2D primitive recognition and in the structure of a description of these primitives significantly reduce the human model of 2D shape recognition and do not use these features in full measure. Thus, an importance of involving these features in the process of the recognition of 2D primitives is great. 1. STATEMENT OF THE PROBLEM Let א be a subject area of the images being recognized and P = { P i be a set of classes of 2D primitives that form a structure of images containing in א . Let us formulate the following problem. Problem * . It is necessary to create a method for constructing the formalized descriptions R '( P i ) of 2D primitives P i . This method must satisfy the following requirements: (i) to be effective; (ii) not to be connected with a primitive shape; (iii) to reproduce all principal psychophysical features of the human model for recognition of 2D shapes. Let I ∈ א be an image being recognized and S be a collection of segments (closed contours) that were constructed in a process of segmentation of I . A contradiction between the requirements and the complexity of constructing 2D shape descriptions that satisfy the requirements of the problem (*) as well as a contradiction between the standard mathematical methods and the efficiency of representation of 2D shapes in human mind require the special methodology. This methodology must allow the recognition system to consult the human expert about the knowledge on 2D shapes, that is, to include the expert knowledge in the descriptions of 2D primitives provided that this knowledge is received in a language similar to the natural language (in a quasi-NL). It assumes the solution of the following tasks: (1) Computer-based formalization of knowledge about 2D primitives received from experts in a quasi-NL. } i 1 n , = REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES