Edward A. Manykin

Oscillatory Network for Synchronization-Based Adaptive Image Segmentation

Oscillatory network model with controllable oscillator dynamics and self-organized dynamical coupling has been created for synchronization-based image processing. The model was previously obtained via reduction from a biologically motivated oscillatory model of the brain primary visual cortex. The reduced network model performance consists in network relaxation into the state of synchronization. The set of internally synchronized but mutually desynchronized network ensembles (clusters), arising at final synchronization state, corresponds to full set of image fragments. New model developments, presented in the paper, include: a) the advanced version of single oscillator dynamics, admitting introduction of arbitrary continuous dependence of oscillator limit cycle size on pixel brightness; b) new principle of network coupling, permitting to raise image segmentation accuracy and to control network noise reduction. A capability of selective image segmentation (extraction of image fragment subset of a priori prescribed brightness levels) is also inherent to current model version.

Optical-digital correlator with increased dynamic range using spatially varying pixels exposure technique

In this work the application of the spatially varying pixels exposure technique for obtaining linear high dynamic range (HDR) images of correlation signals by Bayer-covered photo sensors is presented. Bayer colour filters array is considered as an array of attenuating filters in the quasimonochromatic light. The procedure of HDR images reconstruction using data from neighbour pixels and preliminary obtained correction coefficients is described. Experimental results of HDR registration of correlation signals are provided. It is shown that reconstructed HDR correlation signals are linear as normal signals are. The increase of dynamic range of signal’s registration from 58 dB up to 73 dB is obtained. Results on recognition of test objects with normal and HDR registration are discussed.

Higher-order neural network and photon-echo effect

Abstract Properties of the photon-echo effect are theoretically investigated for implementation of higher-order neural networks. An optical scheme of the second-order neural network is proposed on this basis. The importance of an additional time coordinate inherent in the photon-echo effect is shown.

Linear methods for input scenes restoration from signals of optical-digital pattern recognition correlator

Linear methods of restoration of input scenes images in optical-digital correlators are described. Relatively low signal to noise ratio of a cameras photo sensor and extensional PSFs size are special features of considered optical-digital correlator. RAW-files of real correlation signals obtained by digital photo sensor were used for input scenes images restoration. It is shown that modified evolution method, which employs regularization by Tikhonov, is better among linear deconvolution methods. As a regularization term, an inverse signal to noise ratio as a function of spatial frequencies was used. For additional improvement of restorations quality, noise analysis of boundary areas of the image to be reconstructed was performed. Experimental results on digital restoration of input scenes images are presented.

A model of pulsed oscillator network and the perspectives of its application to the problems of information routing in wireless sensor networks

Development of performance principles of wireless sensor networks (WSN) and the methods of information routing in the networks, providing a capability of self-organized and automatic style of the WSN network work, is currently of substantial interest. The attraction of associated model of pulsed oscillator networks seems to be helpful for the design of adaptive synchronization-based information routing in the WSN. An oscillatory network model with pulsed oscillator dynamics and pulsed oscillator interaction is proposed. The initial version of information transfer in the WSN with the help of synchronization of an associated oscillatory network is discussed.

An oscillatory network model with controllable synchronization and a neuromorphic dynamical method of information processing

A spatially two-dimensional oscillatory neural network model with inhomogeneous modifiable oscillatory coupling is designed and an adaptive dynamical method of brightness image segmentation (image reconstruction) based on self-organized cluster synchronization in the oscillatory network is developed. The method imitates the known phenomenon of dynamical binding via synchronization that is presumably used by a number of the brain neural structures in their work. The oscillatory-network approach demonstrates the following capabilities: (1) high-quality segmentation of real grey-level and color images; (2) selective image segmentation (exclusion of unnecessary information); (3) solution of the simplest problem of object selection in a visual scene—the problem of the successive selection of all spatially separated image fragments of almost equal brightness.

Oscillatory neural network for adaptive dynamical image processing

We develop a biologically motivated oscillatory network model and related dynamical synchronization-based method of image segmentation. The first version of successive segmentation algorithm was based on coupling adaptation in the oscillatory network. New model developments, presented in the paper, include: 1) a modified version of single oscillator dynamics; 2) new network connectivity rule. These modifications permit to significantly improve the oscillatory method capabilities, providing image processing with significantly larger pixel array sizes and ensuring higher segmentation accuracy. In addition the improved network model allows to perform selective image segmentation tasks (extraction of prescribed subset of image fragments). New method capabilities have been demonstrated in computer experiments