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In the domain of material science, quantitative fractography is an analytical tool to study the characteristics of a fracture surface. The inception of Scanning Electron Microscope (SEM) has motivated the researchers toward the quantitative analysis of such surface. Due to fracture, new surfaces are evolved and voids are also formed. Extraction of such regions (surface/void) from SEM fractographs...
In this paper, we propose a novel robust unsupervised image content understanding approach that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighbourhood relationships. Here, automatic hierarchical discovery of classes or clusters in images takes place rather than generating...
Cellular neural network (CNN) algorithms have been successfully used in a plethora of image processing applications including the medical imaging domain. Analogic CNN algorithms use CNN templates combined with logic operations to perform operations such as blurring and thresholding for image processing. In this paper we apply CNN based techniques incorporating image enhancement, region segmentation...
Ultrasound imaging systems use a scan conversion process to display data acquired with polar coordinate in Cartesian coordinate system. To avoid artifacts interpolation is needed for unsampled pixels. The resultant image quality is dependent on the interpolation algorithm. Majority of the interpolation mechanisms enhance both smooth and detailed regions which degrades the image quality. In this paper,...
We address the problem of video super resolution - obtaining a single high-resolution video whose spatial and temporal resolutions are higher than any of the input low-resolution videos. We employ a reconstruction based approach using MRF -MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We show that in our formulation it is possible to selectively...
In this paper, we address the problem of unsupervised learning of usual patterns of activities in an area under surveillance and detecting deviant patterns. We use video epitomes for segmenting foreground objects from background and obtain an approximate shape, trajectory and temporal information in the form of space-time patches. We apply pLSA for finding correlations among these patches to learn...
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