Image processing and machine vision

Computer vision is a field of artificial intelligence that examines and analyzes images and videos using computer algorithms and methods. The main purpose of computer vision is to provide recognition and interpretation of visual scenes by computer systems. One of the main challenges of computer vision is extracting and recognizing meaningful features from images.


To realize this goal, various methods such as neuron behaviorists, Fourier transform, Gabor filters, and Hough transform are used. These methods can extract important features such as edges, key points, colors, forms and templates from images with the help of mathematical and physical operations. One of the most important methods used in computer vision is artificial neural networks (ANNs). Artificial neural networks are a model of the neural structure of the living human system that is imitated by machine learning algorithms.


These networks with a large number of artificial neurons and different layers of weights process the inputs and produce the desired output. By training an artificial neural network, they can recognize patterns and features associated with different categories.

The most pervasive approaches used in computer vision

It is deep neural networks. These networks, with a deep structure and a high number of layers, have the superior ability to recognize patterns and complex features. Deep neural networks are usually built with several hidden layers and each layer contains a set of programmed neurons.


By learning these networks using large and diverse data sets, the networks can be able to recognize and identify objects, group categories and interpret visual scenes. Also, computer vision has a very important role in the field of autonomous driving. By using computer vision and deep neural networks, facilities such as recognition and marking of cars, recognition of augmented reality (Augmented Reality), recognition of pedestrians who intend to cross the street, and other tasks related to self-driving are performed.