Aleksey Fadeev

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In this paper, we propose a generic approach for representing image texture features in a compact and intuitive way. Our approach, called Dominant Texture Descriptor (DTD), is inspired by the dominant color descriptor. It is based on clustering the local texture features and identifying the dominant components and their spatial distribution. We also present(More)
The purpose of this study is to evaluate a 3D volume reconstruction model for volume rendering. The model is conducted using brain MRI data of Visible Human Project. Particularly MRI T1 data were used. The quality of the developed model is compared with linear interpolation technique. By applying our morphing technique recursively, taking progressively(More)
The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to(More)
This paper describes the Ensemble Possibilistic K-NN algorithm for classification of gene expression profiles into three major cancer categories. In fact, a modification of forward feature selection is proposed to identify relevant feature subsets allowing for multiple possibilistic K-nearest neighbors (pK-NNs) rule experts. First, individual features are(More)
In this paper, we address the problem of transforming relational features into an Euclidian space so that standard classification methods that assume that data is in a vector form could be used. Our approach has three main steps. First, a relational matrix that represents the pair-wise dissimilarities between all objects is constructed. Second, a fuzzy(More)
In this paper, we propose a new general low-level feature representation for audio signals. Our approach, called Dominant Audio Descriptor is inspired by the MPEG-7 Dominant Color Descriptor. It is based on clustering timelocal features and identifying dominant components. The features used to illustrate this approach are the well-known Mel Frequency(More)
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