Abstract
This paper is focused on the development of an adaptive NN-RBF classifier for the recognition of objects. The classifier deals with the problem of continuously changing object characteristics used by the recognition processes. This characteristics change is due to the dynamics of scene-viewer relationship such as resolution and lighting. The approach applies a modified Radial-Basis Function paradigm for model-based object modeling and recognition. On-line adaptation of these models is developed and works in a closed loop with the object recognition system to perceive discrepancies between object models and varying object characteristics. The on-line adaptation employs four model evolution behaviors in adapting the classifier’s structure and parameters. The paper also proposes that the models modified through the on-line adaptation be analyzed by an off-line model evaluation method (Gaussian mixture density estimates).
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Baik, S.W., Ahn, S., Pachowicz, P.W. (2000). Evaluation of Adaptive NN-RBF Classifier Using Gaussian Mixture Density Estimates. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_47
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DOI: https://doi.org/10.1007/3-540-45482-9_47
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