Vladimir Mashtalir

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Methods of nonstationary nonlinear time series forecasting under bounded a priori information provide an interdisciplinary applications area that is concerned with learning and adaptation of solutions from a traditional artificial intelligence point of view. It is extremely difficult to solve this type of problems in its general form, therefore, an approach(More)
As the volume of image data and the need of using it in various applications is growing significantly in the last days it brings a necessity of retrieval efficiency and effectiveness. Unfortunately, existing indexing methods are not applicable to a wide range of problem-oriented fields due to their operating time limitations and strong dependency on the(More)
Increasing of multimedia applications in commerce, biometrics, science, entertainments etc. leads to a great need of processing of digital visual content stored in very large databases. Many systems combine visual features and metadata analysis to solve the semantic gap between low-level visual features and high-level human concept, i.e. there arises a(More)
The explosion of image content is closely connected with segmentations efficiency. However, there is no agreement as to what a good segmentation is due to hard data and applications dependence. To reduce the gap between low-level features and high-level semantic, collections of image partitions produced by different segmentation algorithms are often(More)
There are two substantial open issues in the field of the image retrieval: semantic gap between computationally extracted low-level features and human operated high-level concepts, and high retrieval speed independent from the volume of the database. Search of the images on the level of objects or regions (segmentation) is a step towards semantic-based(More)
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