Abstract
In this work, a generalization of non-uniform sampling technique to construct appearance-based models is proposed. This technique analyses the object appearance defined by several parameters of variability, determining how many and which images are required to model appearance, with a given precision ε. Throughout non-uniform sampling, we obtain a guideline to spend less time on model construction and to diminish storage, when pose estimation no matters. The proposed technique is based on a scheme of N-linear interpolation and SSD (Sum-of-Squared-Difference) distance, and it is used in conjunction with the eigenspaces method for object recognition. Experimental results showing the advantages are exposed.
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Altamirano, L.C., Altamirano Robles, L., Alvarado, M. (2004). Generation of N-Parametric Appearance-Based Models Through Non-uniform Sampling. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_16
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DOI: https://doi.org/10.1007/978-3-540-30463-0_16
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