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Text line segmentation plays a vital role in the overall performance of a document recognition system. In the literature, similar segmentation works for offline handwritten Bangla documents are rarely found. On the other hand, certain peculiarities of handwritten Bangla script such as widespread occurrences of ascenders and descenders or some of its characters appearing only as an ascender or descender...
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...
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...
This paper aims at quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. The algorithm involves median and low pass filtering, segmentation by adaptive region growing, optimal and local thresholding, morphological operations such as opening and closing of gray scale and binary images and some numerical methods. Differentiation on...
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