Otar Tavdishvili

One of the approaches to the shape analysis of the extracted segment on 2-D segmented digital image is based on its description by the points of the closed contour surrounding the segment. In simple case, the shape contour can be described by a finite set of its boundary points, for example, a sequence of the coordinates of the contour pixels. At the same time, the larger the number of points the more accurate is the contour description. But this requires a high computational cost for further process of the shape analysis. Therefore, it is very important to obtain a more accurate restoration of the original digital closed contour for the current number of pixels on the contour than using the Whittaker-Kotelnikov-Shannon interpolation formula. In this paper we use the generalized interpolation formula (Piranashvili formula) for solution of the task.

Unsupervised cluster analysis is proposed for the study of active avoidance formation in three groups of albino rats across learning: (a) Intact; with electrolytic lesions of (b) neocortex over the dorsal hippocampus, and (c) dorsal hippocampus. Animals’ learning abilities assessed by acquisition of active avoidance were found to vary within the test groups. Some animals were not able to meet learning criteria and consequently it should be different groups of animals with different behavior, i.e. the groups into which the animals with resembling behavior should be involved. For identification of such groups in three populations of white rats: (a) Intact (INT); with electrolytic coagulation of (b) neocortex above the dorsal hippocampus (NCC) and (c) dorsal hippocampus (DHPC) the method of automatic classification has been applied. The objective of the work was to approve compliance of unsupervised cluster analysis method for quantitative description of behavioral conformities through active avoidance acquisition in different population of albino rats. Such approach enables to classify the animals through the learning process into groups by the degree of behavioral similarity. For quantitative assessment the rats’ behavior across learning processes the term ‘behavior vector’ has been introduced. The behavioral parameters (features) getting different numerical values during the experiment form the components for the behavior vector. The proposed method is convenient to assess learning capacities in animals and makes ground for getting additional information concerning correlative relationships between their learning skills and other neuroethological and neurobiological parameters. 

Unsupervised cluster analysis is proposed for analysis of active avoidance formation in three groups of albino rats: (a) Intact; (b) neocortex  and (c) dorsal hippocampus lesioned. The term ‘behavior vector’ has been introduced to assess quantitatively the behavior of rats while learning. The proposed approach enables to assess active avoidance behavior in rats simultaneously by all the tested parameters and classify the animals into groups by their behavioral resemblance through the learning process.

The segments extracted according with developed non-parametric segmentation method based on Parzen estimation function is used for further scene analysis on a level of segmented image. Obtained segments quasi description gives possibility to reconstruct segments similar to its original form and receive definite information concern objects, their characters, location, etc. It allows making some preliminary conclusion for scene analysis. 

This paper presents segmentation method of extraction 3D segments on scene digital image based on non-parametric statistical estimation of probability density function. Definitions of the modes, both the centers and the radiuses of sameness as greatest boundary of detected clusters are given. The concepts of clusters and connectivity for digital image are suggested. The method does not require initial classification of the data and supposed number of clusters. Execution of the segmentation procedure does not require an interactive entry of parameters based on heuristics. The statistical procedure of adequate selection of mathematical model is developed.

Doctoral Thesis Referee

Master Theses Supervisor

Doctoral Thesis Supervisor/Co-supervisor

Scientific editor of monographs in foreign languages

Scientific editor of a monograph in Georgian

Editor-in-Chief of a peer-reviewed or professional journal / proceedings

Review of a scientific professional journal / proceedings

Member of the editorial board of a peer-reviewed scientific or professional journal / proceedings

Participation in a project / grant funded by an international organization

Participation in a project / grant funded from the state budget

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Membership of the Georgian National Academy of Science or Georgian Academy of Agricultural Sciences

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National Award / Sectoral Award, Order, Medal, etc.

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Monograph

Handbook

Research articles in high impact factor and local Scientific Journals

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