Abstract
The non-uniform sampling of appearance–based models supported by neural networks is proposed. By using the strictly required images –obtained by applying non-uniform sampling- for modeling an object, a significant time reduction for the training process of neural networks is achieved. In addition, high levels of recognition are obtained.
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© 2004 Springer-Verlag Berlin Heidelberg
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Altamirano, L.C., Alvarado, M. (2004). Optimized Object Recognition Based on Neural Networks Via Non-uniform Sampling of Appearance-Based Models. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_61
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DOI: https://doi.org/10.1007/978-3-540-30498-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23806-5
Online ISBN: 978-3-540-30498-2
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