Abstract
This paper presents a novel approach to automatically recognize objects. The system used is a new model that contains two blocks; one for extracting direction and pixel features from object images using Cellular Neural Networks (CNN), and the other for classification of objects using a General Regression Neural Network (GRNN). A data set consisting of different properties of 10 different objects is prepared by CNN.
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Polat, Ö., Tavşanoğlu, V. (2006). 3-D Object Recognition Using 2-D Poses Processed by CNNs and a GRNN. In: Savacı, F.A. (eds) Artificial Intelligence and Neural Networks. TAINN 2005. Lecture Notes in Computer Science(), vol 3949. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11803089_26
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DOI: https://doi.org/10.1007/11803089_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36713-0
Online ISBN: 978-3-540-36861-8
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