Abstract
Artificial neural networks are an attempt to emulate the processing capabilities of biological neural systems. The basic idea is to realize systems capable of performing complex processing tasks by interconnecting a high number of very simple processing elements that might even work in parallel. They solve cumbersome and intractable problems by learning directly from data. An artificially neural network usually consists of a large amount of simple processing units, namely neurons, via mutual interconnection. It learns to solve problems by adequately adjusting the strength of the interconnections according to input data. It is easily adapted to new environments by learning. These features motivate extensive researches and developments in artificial neural networks. The main features of artificial neural networks are their massive parallel processing architectures and the capabilities of learning from the presented inputs. They can be utilized to perform a specific task only by means of adequately adjusting the connection weights, that is, by training them with the presented data. Learning is done in accordance with the direct comparison of the actual output of the network with known correct answers. It is also referred to as learning with a teacher. Neural networks have been successfully employed to solve a variety of computer vision problems. They are systems of interconnected simple processing elements. There exist many types of neural networks that solve a wide range of problems in the area of image processing.
Published Version
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