An Adaptive Two-Stage BPNN–DCT Image Compression Technique

A mulit-layer artificial neural network, with a backpropagation training algorithm and 11 neurons in the input layer, was used.. In this type of network, network inputs are

Pao and Takefuji (1992) presented a Random Vector Functional-Link (RVFL) which is given by one hidden layer feedforward neural network with randomly selected input-to-hidden weights

The DWT is applied for these images, trained with the neural network method and then proposed watermarking technique is applied to get the watermarked image as shown in

The number of neurons in input layer, hidden layer and output layer of this neural network were kept as 6, 11 and 1, respectively (Fig. This ANN was first trained with

Three feed-forward neural network models characterized by a similar structure (five neurons in the input layer, one hidden layer and one neuron in the output layer) are

In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression

Image compression is a mature field in terms of research but still a very few research work mentions the time complexity for their methods, Table 6 is compiled for the

An Unsupervised Dynamic Image Segmentation using Fuzzy Hopfield Neural Network based Genetic Algorithm1. Amiya Halder 1 and Soumajit