NDLI: Using moment invariants for classifying shapes on large-scale maps

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Using moment invariants for classifying shapes on large-scale maps

Content Provider	Semantic Scholar
Author	Keyes, Laura Winstanley, Adam C.
Copyright Year	2001
Abstract	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 shape 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.
Starting Page	119
Ending Page	130
Page Count	12
File Format	PDF HTM / HTML
DOI	10.1016/S0198-9715(00)00041-7
Volume Number	25
Alternate Webpage(s)	http://eprints.maynoothuniversity.ie/64/1/ceus.pdf
Alternate Webpage(s)	https://www.researchgate.net/profile/A_Winstanley/publication/222685312_Using_moment_invariants_for_classifying_shapes_on_large-scale_maps/links/00b7d517b7888107a0000000.pdf
Alternate Webpage(s)	https://doi.org/10.1016/S0198-9715%2800%2900041-7
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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