Leonid I. Timchenko

Method for training of a parallel-hierarchical network based on population coding for processing of extended laser path images

Authors have worked out a nonstationary signal analysis method on an example of research laser lines. This method disclosed relationship between signal approximation coefficients and geometry signal characterizations (for instance, energy center, moment of inertia). An example, which is demonstrating an application of this method for exact coordinate determination problem in laser line at displacement compensation in laser imaging are present.

Analysis of the methodological approaches in connection with problem solving the extrapolation of object trajectory

Authors have worked out a nonstationary signal analysis method on the example of research of laser lines. This method disclosed relationship between signal approximation coefficients and geometry signal characterizations(for instance, energy center, moment of inertia). The example, which is demonstrating an application of this method for exact coordinate determination problem in laser line at displacement compensation in laser imaging are present. Various extrapolation approaches of laser beam location in real time are considered.

Laser beam image classification methods with the use of parallel-hierarchical networks running on a programmable logic device

Abstract. We examine the methods of laser beam classification and their uses. We discuss the necessity of noise filters using adaptive methods in beam reflection, such as parallel-hierarchical networks. A demonstration of this network is shown on a programmable logic device.

Modeling of a method of parallel hierarchical transformation for fast recognition of dynamic images

Principles necessary to develop a method and computational facilities for the parallel hierarchical transformation based on high-performance GPUs are discussed in the paper. Mathematic models of the parallel hierarchical (PH) network training for the transformation and a PH network training method for recognition of dynamic images are developed.

Bio-inspired approach to multistage image processing

Multistage integration of visual information in the brain allows people to respond quickly to most significant stimuli while preserving the ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing, described in this paper, comprises main types of cortical multistage convergence. One of these types occurs within each visual pathway and the other between the pathways. This approach maps input images into a flexible hierarchy which reflects the complexity of the image data. The procedures of temporal image decomposition and hierarchy formation are described in mathematical terms. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image which encapsulates, in a computer manner, structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a very quick response from the system. The result is represented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match.

New approach for the detection of noise-distorted signals based on the method of S-preparation

The article describes method of S-preparation, which is characterized by high noise immunity and adaptability to uncertainty and variability of the signal-jamming environment, due to the formation of pre-pipelined convolution sums of correlated images.

Compact representation of the facial images for identification in a parallel-hierarchical network

The work offers the methods for invariant representation of images against a variety of distorting factors including 2D and 3D rotation, changes in brightness, contrast and scale. The problems of preliminary image processing based on the method of generalized Q-transformation are being solved. The calculating algorithms based on the methodology of dichotomous balance of the images being prepared have been used for the classification of human facial images. It also deals with the procedure of recursive contour preparation consisting of step-by-step preparation of differences among the pixels of grey-scale image and formation of positive, negative and zero preparations. Thus at the first step the contour preparation is effected for the first differences, at the second step, for the second differences, and so on, with a step-by-step definition of the criterial function of distribution of binarized preparations. So it is possible to identify objects in different lighting conditions which simplifies the implementation of similar approaches. This relative simplicity of this method extends the range of its possible application for recognition purposes and for its implementation in the parallel-hierarchical network in particular.

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

Architecture of the parallel hierarchical network for fast image recognition

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

Coordinate reference system of nonstationary signals

Authors have worked out a nonstationary signal analysis method on an example of research of laser lines. This method disclosed relationship between signal approximation coefficients and geometry signal characterizations (for instance, energy center, moment of inertia). Examples, which is demonstrating an application of this method for exact coordinate determination problem in laser line at displacement compensation in laser imaging are present.