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    A new gaze points agglomerative clustering algorithm and its application in regions of interest extraction

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    Date
    2014-02-21
    Author
    Al-Azawi, M.;
    Yang, Yingjie;
    Istance, Howell
    Metadata
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    Abstract
    In computer vision applications it is necessary to extract the regions of interest in order to reduce the search space and to improve image contents identification. Human-Oriented Regions of Interest can be extracted by collecting some feedback from the user. The feedback usually provided by the user by giving different ranks for the identified regions in the image. This rank is then used to adapt the identification process. Nowadays eye tracking technology is widely used in different applications, one of the suggested applications is by using the data collected from the eye-tracking device, which represents the user gaze points in extracting the regions of interest. In this paper we shall introduce a new agglomerative clustering algorithm which uses blobs extraction technique and statistical measures in clustering the gaze points obtained from the eye tracker. The algorithm is fully automatic, which means does not need any human intervention to specify the stopping criterion. In the suggested algorithm the points are replaced with small regions (blobs) then these blobs are grouped together to form a cloud, from which the interesting regions are constructed.
    Description
    Citation : Alazawi, M., Yang, Y. and Instance, H. (2014) A new gaze points agglomerative clustering algorithm and its application in regions of interest extraction. In Proceedings of the 2014 IEEE Advance Computing Conference (IACC), Gurgaon. pp. 946-951
    URI
    http://hdl.handle.net/2086/11721
    DOI
    http://dx.doi.org/10.1109/IAdCC.2014.6779450
    ISBN : 9781479925711
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2679]

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