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This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation...
This paper reports on the results obtained when using Radial Basis Neural Networks to classify different objects using only colour data extracted from images captured under different lighting conditions. Each network is trained with data from a single image and then tested with data from images containing the same collection of objects, but captured under different lighting conditions. The ability...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and in this instance, RL techniques require the use of function approximators for learning value functions and policies. Often, local linear models have been preferred over distributed nonlinear models for function approximation in RL. We suggest that one reason for the difficulties encountered when using...
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