Leaf Classification Using Convexity Moments of Polygons
Kala, J. R., S. Viriri and D. Moodley (2016) Leaf Classification Using Convexity Moments of Polygons. In Proceedings 12th International Symposium, ISVC 2016, Advances in Visual Computing, Lecture Notes in Computer Science 10073, pages 330-339, Las Vegas, USA.
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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).
|EPrint Type:||Conference Poster|
|Subjects:||I Computing Methodologies: I.4 IMAGE PROCESSING AND COMPUTER VISION|
|Deposited By:||Moodley, Deshen|
|Deposited On:||30 December 2016|