Artificial Neural Networks have broad applications to the real world business problems. They have already been successfully applied in many industries. Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting. These include Sales forecasting, Industrial process control, Customer research, Data validation, Risk management, Target marketing. The work studies the use of Artificial Neural Network in the field of Image Processing. One of the applications studied is the edge detection process. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. The work demonstrates both entropy and Neural Network based edge detection methods, where Renyi's Entropy and Convolutional Neural Network based edge detection is proposed and their results are compared.

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