Abstract
New efficient 1-D and 2-D statistical moments are presented, which are invariant under translation, rotation and scaling change of the shape. These new moments constitute an efficient set of features, appear to have better classification performance over the existing sets of moments and are applicable to any kind of shapes (open, closed, or with holes). The shape discrimination and classification is achieved by testing a weighed least square cost function. The proposed techique is applied for classification and discrimination of industrial objects, in robotic vision applications.
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© 1993 Springer Science+Business Media New York
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Mertzios, B.G. (1993). Shape Discrimination and Classification in Robotic Vision Using Scaled Normalized Central Moments. In: Kárný, M., Warwick, K. (eds) Mutual Impact of Computing Power and Control Theory. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2968-2_21
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DOI: https://doi.org/10.1007/978-1-4615-2968-2_21
Publisher Name: Springer, Boston, MA
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