John Blake


John Blake

UAM Corpus Tool Move Highlighter (Use in Google Chrome)

This highlighter uses regular expressions to search your submitted text for a specific set of tags created in UAM Corpus Tool and highlights each tag in different colours. The moves searched for are classified into five groups:

  • Introduction - Red
  • Purpose - Orange
  • Method - Yellow
  • Results - Green
  • Discussion - Blue
  • How to use the highlighter

    1. Paste your text into the box below.
    2. Click on Submit.

    Note: This tool is designed to work in Google Chrome, but may work in other browsers.

    Test abstract

    <?xml version='1.0' encoding='utf-8'?>
    <textfile>Trans on Image Processing/Tr_on_ImageProcess_1.txt</textfile>
    <segment features='problem;introduction;rhetorical_moves' state='active'>We address the problem of model-based object recognition.</segment> <segment features='purpose;rhetorical_moves' state='active'>Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes.</segment> <segment features='method;rhetorical_moves' state='active'>A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints.</segment> <segment features='purpose;rhetorical_moves' state='active'>An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution.</segment> <segment features='background;introduction;rhetorical_moves' state='active'>The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition.</segment> <segment features='method;rhetorical_moves' state='active'>Numerous experiments are conducted in this paper to demonstrate the performance of our approach.</segment> <segment features='methodasproduct;result;rhetorical_moves' state='active'>It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data.</segment> <segment features='resultasproduct;result;rhetorical_moves' state='active'>The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.</segment> </body>

    Last updated on 8 July 2015.

    (c) John Blake 2015 with all credit for initial coding to Andy Morrall.