It is proposed an image complexity matrix (IC), based on the analysis of the Fourier spectrum of the
input image. It is described the method of IC calculation. It was carried out the determination of the
necessary number of the image's pixels dependent on the image complexity. It is presented the
optical - electronic processor for IC determination. It is analyzed the structure of the optical
electronic computer system for pattern and target recognition.
This work contains the results of the experiments on the restoration of the defective images
proceeded in a matrix and a vector form with the help of the feed forward neural network.
Sometimes it is convenient to represent an image as a vector rather than as a matrix. So the
target of this work is to show experimentally what kind of input provides a better restoration,
judging from the Euclid's distance of the output of a trained network. This work also shows the
differences between processing different types of image presentation of the neuron network.
Making a comparative analysis of a matrix and a vector form of presenting the images which are
proceeded to a feed forward network allows stating some specific characteristics of a network.
These characteristics include the optimal architecture of a network, the number of layers, the
number of neurons in each layer and the time of an image restoration. Taking into account the
network's characteristics and the most important factor - the Euclid's distance, are drawn
conclusions that concern what is the best way of representing images that we want to restore
using a feed forward network.
There are presented the results of investigation of the algorithms of invariant face recognition of masked persons. There
are described 3 algorithms based on Image Moments Features, Principal Component Analyses algorithm and
Correlation algorithm. It is presented the description of the elaborated software for PC based face recognition, created
in Borland C++ Builder environment. There are presented the data of the face recognition in conditions of masking,
change of the rotation, scale of the images.
It is analyzed the important and actual problem of the defective images of scenes restoration. The proposed
approach provides restoration of scenes by a system on the basis of human intelligence phenomena reproduction
used for restoration-recognition of images. The cognitive models of the restoration process are elaborated. The
models are realized by the intellectual processors constructed on the base of neural networks and associative
memory using neural network simulator NNToolbox from MATLAB 7.0. The models provides restoration and
semantic designing of images of scenes under defective images of the separate objects.
This paper deals with the problem of intellectual restoration of images. It is suggested to represent various objects and
stages as objects of the first and second orders. Representation of dominant object as second order object reveals its new
properties, that is an opportunity to control its own parameters. Complex representation of dominant object as second-class
object of the first and second types allows to eliminate defects of its own image, as well as defects of image of
A method of the recognition reliability estimation in the optical pattern recognition systems (OPRS) is
described, based on of the similarity measures differences (SMD). It was theoretically justified and
experimentally confirmed a hypothesis about the distribution law of the SMD. There were calculated the
reliabilities of the correct objects recognition at single and coded correlation responses in OPRS of invariant
and normalized images processing.
Intelligence systems on basis of artificial neural networks and associative memory allow to solve effectively problems of recognition and restoration of images. However, within analytical technologies there are no dominating approaches of deciding of intellectual problems. Choice of the best technology depends on nature of problem, features of objects, volume of represented information about the object, number of classes of objects, etc. It is required to determine opportunities, preconditions and field of application of neural networks and associative memory for decision of problem of restoration of images and to use their supplementary benefits for further development of intelligence systems.
The field of researches is connected with problems of restoration of images on the incomplete information of objects, which are represented in the digital image form. Questions of application of artificial intelligence systems for image restoration are considered.