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This study is about object matching, that is, a problem of locating the corresponding points of an object in an image. Conventional approaches to object matching are batch methods, meaning that the methods first learn the object model from a training set of example images that contain instances of the object, and then use the learned object model to match instances of the same object (or object class)...
In batch learning all the training examples have to be available at once to train the model, which often leads to slow performance and large memory requirements. Little work has been done in developing incremental object learners. In this paper, we present an incremental method that finds corresponding points of similar object instances, appearing in natural grayscale images with arbitrary location,...
Selecting automatically feature points of an object appearing in images is a difficult but vital task for learning the feature point based representation of the object model. In this work we present an incremental Bayesian model that learns the feature points of an object from natural un-annotated images by matching the corresponding points. The training set is recursively expanded and the model parameters...
This paper concerns the application of differential evolution to training radial basis function networks. The algorithm consists of initial tuning, local tuning, and global tuning. The last two tunings both use a cycle-increased searching scheme, and global tuning employs fuzzy adaptive control. The mean square error from desired to actual outputs is applied as the objective function. Four standard...
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