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    Using moment invariants for classifying shapes on large scale maps

    Keyes, Laura and Winstanley, Adam C. (2001) Using moment invariants for classifying shapes on large scale maps. Computers, Environment and Urban Systems, 25 (1). pp. 119-130. ISSN 0198-9715

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    Automated feature extraction and object recognition are large research areas in the field of image processing and computer vision. Recognition is largely based on the matching of descriptions of shapes. Numerous shapes description techniques have been developed, such as scalar features (dimension, area, number of corners etc.), Fourier descriptors and moment invariants. These techniques numerically describe shapes independent of translation, scale and rotation and can be easily applied to topographical data. The applicability of the moment invariants technique to classify objects on large-scale maps is described. From the test data used, moments are fairly reliable at distinguishing certain classes of topographic object. However, their effectiveness will increase when fused with the results of other techniques.

    Item Type: Article
    Additional Information: This is the preprint version of published article, which is available at
    Keywords: Moment invariants; Fourier descriptors; Large-scale maps; Cartographic data;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 64
    Identification Number: 0.1016/S0198-9715(00)00041-7
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 08 Sep 2006
    Journal or Publication Title: Computers, Environment and Urban Systems
    Publisher: Elsevier
    Refereed: Yes
    Funders: Enterprise Ireland (EI)
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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