Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Mixture structure analysis using the Akaike Information Criterion and the bootstrap
Statistics and Computing
Model modification methodology for adaptive object recognition in a sequence of images
Model modification methodology for adaptive object recognition in a sequence of images
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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).