SCENE UNDERSTANDING USING BACK PROPAGATION BY NEURAL NETWORK

In this paper, we proposed an efficient method to address the problem of scene understanding that is based on neural network (NN) and image segmentation. We utilized a multilayer perceptron (MLP) to train the network and features are extracted using pixels in the RGB color space. In this work, object samples in images with varying lighting conditions are used to obtain a wide object color distribution. The training data is generated from positive and negative training patterns in the color planes. Subsequently, training set is fed to an MLP, trained by the back propagation algorithm using these object samples. We apply the above mentioned NN-based object classifier to the test image which is applied to image segmentation and corresponding to the pixel level of the object in test image particular object is determined

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains