Daniya Zamalieva

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The increase in the amount of available high-resolution remotely sensed data is subsequently causing the augmentation of applications that aim for automatic information extraction and knowledge discovery. One interesting way of enabling high-level understanding about the image content is to identify the significant image regions that are internally(More)
We introduce a new approach to perform background subtraction in moving camera scenarios. Unlike previous treatments of the problem, we do not restrict the camera motion or the scene geometry. The proposed approach relies on Bayesian selection of the transformation that best describes the geometric relation between consecutive frames. Based on the selected(More)
Background subtraction is a commonly used technique in computer vision for detecting objects. While there is an extensive literature regarding background subtraction, most of the existing methods assume that the camera is stationary. This assumption limits their applicability to moving camera scenarios. In this paper, we approach the background subtraction(More)
The constant increase in the amount of Earth observation data has made automatic content extraction and retrieval highly desired goals for effective and efficient processing of remotely sensed imagery. For example, nearly 3 terabytes of data are being sent to Earth by NASA's satellites every day [1]. However, existing systems for accessing remotely sensed(More)
In information retrieval (IR) systems, there are a query and a collection of documents compared with this query and ranked according to a particular similarity measure. Since texts with the same content can be written by different authors, the writing styles of the documents change as well accordingly. This observation brings the idea of investigating text(More)
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