Abstract
Two feature extraction techniques (PCA/ICA) for recognition of 3D objects from appearance are compared with respect to different recognition approaches (universal/object subspaces). A class separation ratio is defined, and several recognition experiments are performed using the COIL-100 database. The results show that both techniques produce similar recognition rates when universal subspaces are used; but, when object subspaces are used, ICA representation greatly outperforms the earlier PCA technique due to its ability to separate classes.
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Vicente, M.A., Fernández, C., Reinoso, O., Payá, L. (2004). 3D Object Recognition from Appearance: PCA Versus ICA Approaches. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_68
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DOI: https://doi.org/10.1007/978-3-540-30125-7_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
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