Leaf Classification Using Convexity Moments of Polygons

Kala, J. R. and Viriri, S. and Moodley, D. (2016) Leaf Classification Using Convexity Moments of Polygons, Proceedings of 12th International Symposium, ISVC 2016, Advances in Visual Computing, Lecture Notes in Computer Science, 12-14 December 2016, Las Vegas, USA, 10073, 330-339, Springer.

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Research has shown that shape features can be used in the process of object recognition with promising results. However, due to a wide variety of shape descriptors, selecting the right one remains a difficult task. This paper presents a new shape recognition feature: Convexity Moment of Polygons. The Convexity Moments of Polygons is derived from the Convexity measure of polygons. A series of experimentations based on FLAVIA images dataset was performed to demonstrate the accuracy of the proposed feature compared to the Convexity measure of polygons in the field of leaf classification. A classification rate of 92% was obtained with the Convexity Moment of Polygons, 80% with the convexity Measure of Polygons using the Radial Basis function neural networks classifier (RBF).

Item Type: Conference poster
Subjects: Computing methodologies > Computer graphics > Image manipulation > Image processing
Alternate Locations: http://link.springer.com/chapter/10.1007/978-3-319-50832-0_32
Date Deposited: 30 Dec 2016
Last Modified: 10 Oct 2019 15:32
URI: http://pubs.cs.uct.ac.za/id/eprint/1133

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