Currently, the main method for determining the output characteristics of optical-electronic image visualization systems (OEIS) is the method of direct experimental research (method of expert assessments). To obtain statistically reliable results, this method requires the involvement of a large number of trained observers. The consequence of this is significant time and economic costs for the creation of working samples of OESVI. When using the method of expert assessments to optimize system parameters, it becomes necessary to create several systems with different parameters or one system in which the parameter under study varies, which makes obtaining experimental results even more expensive.

Elimination of these shortcomings is possible by creating a mathematical model of the OESVI, which includes a mathematical model of the observer’s organ of vision (03). In this case, the volume of necessary experiments is significantly reduced, since they are needed only to find the unknown functions and coefficients included in the model, as well as to determine the limits of its applicability. Mathematical models make it possible not only to significantly reduce the time for researching existing types of OESVI, but also to analyze promising OES without creating prototypes, which significantly reduces time and economic costs.

A feature of most visual work, carried out both with the help of OESVI and with the naked eye, is the presence of a random, uneven distribution of brightness in the field of view. Thus, observation in OESVI can occur under conditions of additive superposition of noise from the electronic path ("additive noise") on a non-random object and background. During field observation, an object (in particular, one with a random coloring), as a rule, applicatively replaces a section of a random background such as a landscape, water surface, clouds, etc., as a result of which the image areas inside and outside the object’s contour cease to be correlated (“ applicative background"). In addition, cases of additive noise and applicative background are observed together.

As an analysis of the literature data has shown, a mathematical description of the process of detection by an observer of objects against real backgrounds that have both additive and applicative components is currently missing, which makes it impossible to develop a mathematical model of OESVI for this case. Therefore, the proposed topic of the dissertation can be considered relevant.

The dissertation consists of a table of contents, introduction, six chapters, conclusions on the work and a list of references.

The first chapter contains a literary analysis of more than 100 different models of human threshold vision, indicating their advantages and disadvantages. A classification of such models is proposed, according to which four approaches to their creation can be distinguished: empirical, physiological, informational and statistical. Based on the results obtained, we can conclude that the statistical approach is most promising for solving the formulated problem.

In the second chapter, a structural diagram of the statistical model 03 is proposed based on the likelihood ratio function, and calculated relations are obtained that allow one to find the probability of detecting objects and recognizing pairs of objects by an observer on random backgrounds, taking into account the joint influence of the applicative background, additive noise and masking coloring of the object. The obtained relationships also make it possible to take into account the influence of the optical elements of the OESVI and 03 observer on the calculation results using the model.

At the beginning of the third chapter, an analytical description of the scattering function of the eye optics point is obtained, based on the results of experiments of a number of authors and allowing to take into account the real properties of the eye optics in model 03. Next, an analysis of the operation of the visual analyzer at high brightness levels of adaptation is carried out and the process of obtaining the functional dependence of the distribution of retinal sensitivity is considered on the brightness of the background and the distance from the center of the fovea. The software created for this purpose is described. This dependence is an important component of the model, which allows us to move from the distribution of brightness in the space of objects to the distribution of receptor reactions across the retina 03.

The fourth chapter includes a description of a package of application programs that implement the developed model 03 and allow one to calculate the probability of detecting objects and identifying pairs of objects on random backgrounds.

The fifth chapter outlines the features of representing continuous functions in a computer and substantiates the need to take into account the errors that arise during calculations using the DETECTOR program, which implements the created model 03. A method for estimating these errors, caused by both coarse discretization and cutting at the edge of arrays of functions included in into the calculated expressions of the model. This technique allows you to select a sampling interval that minimizes the total error of calculations using the DETECTOR program.

The sixth chapter contains a description of the experimental research method and setup scheme for determining the probability of detecting objects and recognizing the orientation of objects by an observer against random backgrounds. The specified method and scheme are intended to test the performance of the proposed model 03 under various observation conditions 8. A description of the software of the experimental setup and the methodology for its calibration is given. Methods for conducting visual experiments and assessing the error of the results are also described. A comparison of the calculation results using the model with experimental data showed their agreement with a confidence level of 0.9 in a wide range of observation conditions.

The results of the dissertation work were published in six printed works, tested at the International Scientific and Technical Conference "0lighting"9b" (Varna, 1996), the Moscow Student Scientific and Technical Conference "Radio Electronics and Electrical Engineering in the National Economy" (MPEI, 1997), the III International Lighting Engineering conference (Novgorod, 1997) and scientific seminars of the Lighting Engineering Department of MPEI.

CONCLUSIONS ON THE WORK

1. The most significant results in the development of 03 models for solving the problem of detecting objects on random backgrounds were obtained on the basis of TCP, however, in the literature there is no description of 03 models for solving the problem of detecting objects on real backgrounds that have both additive and applicative components.

2. A statistical model 03 has been developed, which allows one to calculate the probability of detecting an object taking into account the characteristics of the object, additive noise and applicative background. The performance of model 03 on random backgrounds, i.e., in conditions fundamentally different from the conditions of its normalization, indicates that the algorithm embedded in the model is quite close to that implemented by the organ of vision.

3. It is shown that to calculate the probability of detecting objects against real backgrounds, it is enough to determine only two characteristics of model 03: the threshold likelihood ratio and the functional dependence of retinal sensitivity on adaptation brightness and distance from the center of the fovea. A comparison of the calculation and experimental results indicates the constancy of the threshold likelihood ratio with variations in the average background brightness, the brightness distribution over the object, and the statistical characteristics of the observed images.

4. Multivariate calculations using model 03, implemented in the form of an application software package, showed a significant influence of the sampling interval of the functions included in the calculation expressions of the model on the calculation results. The developed method for estimating the calculation error makes it possible to select a sampling interval that gives the minimum value of this error, which for typical observation conditions does not exceed 20%.

5. It is shown that when observing on a fine-textured background, a rational choice of object coloring can always achieve its effective camouflage, whereas when observing on a strongly correlated background, it is impossible to camouflage an object by randomly coloring it. An increase in the dispersion of the applicative background leads first to a decrease in the probability of detecting objects, and then to its increase. Increasing the dispersion of additive noise can only worsen the conditions for detecting objects.

6. The developed experimental setup, combining electronic and optical methods of image formation, made it possible to obtain the dependence of the probability of detecting objects on observation conditions with an error of no more than 17%. It is shown that the calculation results using the developed model 03 coincide with the experimental results with a confidence probability of 0.9.

7. The results obtained show that the application of the developed model 03 to calculating the probability of detecting objects is legitimate when the background brightness is 10~2.102 cd/m2 and the angular sizes of objects are O.Yu0. Good agreement between the calculated and experimental dependences is observed when the statistical characteristics of the images vary in the following range:

Additive noise correlation interval 0.30";

Correlation interval of applicative background 0.80";

Object masking correlation interval 0.30";

Relative standard deviation of additive noise o.0.1;

Relative standard deviation of the applicative background o.0.14;

Relative standard deviation of object camouflage is about 0.05.

8. A computational and experimental study of the dependences of the probability of recognizing pairs of objects on observation conditions confirms the hypothesis that detecting an object and recognizing a pair of objects are equivalent tasks for the observer.

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