![]() The time taken to train the neural network may get high in some cases. This ends the training process of the neural network.This cycle of forward and backward propagation is done several times on multiple inputs until the network predicts the output correctly in most of the cases. Based on this information, the weights are adjusted.This information is then transferred back through the network, the process is known as Backpropagation. The predicted output is compared with the actual output to obtain the error.These outputs are the probability values. In the output layer, the neuron with the highest value predicts the output.In this manner, data is propagated through the network, this is known as Forward Propagation. The activated neurons transmits data to the next hidden layers.The output from the hidden layers is passed through an activation function which will determine whether the particular neuron will be activated or not.The inputs are multiplied by the corresponding weights and this weighted sum is then fed as input to the hidden layers.Each of these channels is assigned a numerical value known as weight.Let’s consider an image, each pixel is fed as input to each neuron of the first layer, neurons of one layer are connected to neurons of the next layer through channels.Let’s see the two fundamental operations of morphological image processing, Dilation and Erosion: A structuring element is a small matrix with 0 and 1 values. This technique analyzes an image using a small template known as structuring element which is placed on different possible locations in the image and is compared with the corresponding neighbourhood pixels. It depends on the related ordering of pixels but on their numerical values. ![]() It consists of non-linear operations related to the structure of features of an image. Morphological operations can be extended to grayscale images. It also helps in smoothing the image using opening and closing operations. Morphological image processing tries to remove the imperfections from the binary images because binary regions produced by simple thresholding can be distorted by noise.
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