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Graphical models have been shown to provide a natural framework for modelling high level action transition constraints, and to simultaneously segment and recognize a sequence of actions. Spatio-temporal interest points (STIPs) have been proposed as suitable features for action detection. These interest points are typically mapped to a set of codewords, and actions are detected by accumulating the...
Tracking and recognition of objects in video sequences suffer from difficulties in learning appropriate object models. Often a high degree of supervision is required, including manual annotation of many training images. We aim at unsupervised learning of object models and present a novel way to build models based on motion information extracted from video sequences. We require a coarse delineation...
The aim of this paper is to recognize upper-case English alphabets from handwritten documents. An OCR system based on four-sided projections of an alphabet is proposed. The projection points are approximated by polygons and feature points are extracted subsequently for notch elimination and segmentation of the polygon. The resulting segments are smoothed by Bezier approximation. Statistical matching...
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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