Image Recognition Technology in Artificial Intelligence (AI)

The meaning of image recognition technology

Image recognition is an important field of artificial intelligence, which refers to the technology of using computers to process, analyze and understand images in order to recognize various different patterns of targets and pairs of images. In general industrial use, industrial cameras are used to capture images, and then the software is used to do further recognition processing based on the grayscale difference of the images.

In the specific application practice, the special recognition should not only figure out what kind of object the recognized object has but also clarify its location and pose. At present, image recognition has been widely used in various fields, such as license plate number recognition and traffic sign recognition in the traffic field, flight object recognition and terrain survey in the military field, fingerprint recognition and face recognition in the security field, etc.

Principle of image recognition technology

The principle of image recognition is mainly to process information of certain complexity, and the processing technology is not randomly present in the computer, but mainly based on the practice of some medical researchers, combined with the computer program to simulate and realize the relevant content. The computer implementation of this technology is basically similar to the basic principle of human recognition of images, except that the computer is not influenced by any factors in terms of human sensation and vision, etc. Rather than just combining the image memory stored in the mind, humans use the image features to classify them and then use the various types of features to recognize the image. Computers also use the same principle of image recognition, using the classification and extraction of important features of the image and effectively excluding useless and redundant features, which in turn enables image recognition. Sometimes the computer's extraction of the above features is more obvious, and sometimes it is more common, which will have a greater impact on the efficiency of computer image recognition.

The process of image recognition technology

Since the image recognition technology is generated based on artificial intelligence, the process of computer image recognition is roughly the same as the process of image recognition by human brain, and in summary, the process mainly includes 4 steps.

• The acquisition of information, which mainly refers to the conversion of information such as sound and light to electrical signals through sensors, that is, the acquisition of basic information of the recognition object and its conversion to computer-recognizable information.

• Information pre-processing, which mainly refers to the processing of images using operations such as denoising, transforming and smoothing, based on which the important characteristics of the images are improved.

• Extraction and selection of features, mainly refers to the extraction and selection of image features in pattern recognition, in summary, is to identify images with a variety of characteristics, such as the use of a certain way to separate, to identify the characteristics of the image, to obtain features is also known as feature extraction.

• The design of classifier and classification decision, where the design of classifier is based on the training of recognition rules to develop, based on this recognition rules can get the main kinds of features, and then make the image recognition of continuously improve the recognition rate, and thereafter by recognizing the special features, and finally achieve the evaluation and confirmation of the image.

Common forms of image recognition technology

First of all, the development of image recognition has gone through three stages: text recognition, digital image processing and recognition, and object recognition.

Research on text recognition started in 1950, generally recognizing letters, numbers and symbols, with a wide range of applications from printed text recognition to handwritten text recognition.

Research on digital image processing and recognition began in 1965. Digital images have great advantages compared with analog images such as storage, easy transmission compressible, not easy to distort during transmission, easy processing, etc. These have provided a strong impetus for the development of image recognition technology.

Object recognition mainly refers to the perception and recognition of the object and environment in the three-dimensional world, which belongs to the advanced computer vision category. It is a research direction based on digital image processing and recognition combining artificial intelligence, systematic and other disciplines, and its research results are widely used in various industrial and detection robots.

With the rapid development of computer and information technology, the application of image recognition technology gradually expands to many fields, especially in many fields such as facial and fingerprint recognition, satellite cloud map recognition and clinical medical diagnosis increasingly play an important role. Usually, image recognition technology mainly refers to the use of computers to process the front-end pictures of the captured system according to the set target. The application of image recognition technology is also very common in daily life, such as license plate capture, commodity barcode recognition and handwriting recognition. With the gradual development and continuous improvement of this technology, it will have more extensive application fields in the future.

Image recognition technology based on neural network

At present, neural network-based image recognition is a relatively new technology, which is based on the traditional image recognition method and effectively incorporates neural network algorithms. Here, a neural network mainly refers to an artificial neural network, in other words, the neural network in this paper is not the neural network of the animal body, but mainly refers to a kind of neural network in which human adopts an artificial simulation of an animal neural network way. For the image recognition technology based on neural network, at present, in image recognition technology based on neural network, genetic algorithm effectively combined with BP neural network is the most classical model, and this model can be applied in many fields. For example, in the photo recognition technology used in intelligent car monitoring, if there is a car passing by the location, the detection equipment will produce a corresponding response, and the detection equipment starts the image acquisition device to obtain the characteristic images of the front and back of the car, and in the process of recognizing the license plate characters, two types of algorithms based on neural networks and fuzzy matching are used.

Image recognition technology based on nonlinear dimensionality reduction

Computer recognition of images is a recognition technique based on a high-dimensional form. Regardless of the resolution of the original image, the data generated by the image usually has multidimensional characteristics, which increases the difficulty of computer recognition to a certain extent. In order to make the computer's image recognition performance more efficient, using with the image dimensionality reduction method is the most direct and effective method. In general, dimensionality reduction can be divided into two categories: nonlinear and linear. For example, the most common linear dimensionality reduction method is the principal component score and linear singularity analysis, which are characterized by simplicity and easier understanding.

In information technology, as the emerging image recognition technology in recent years has been widely used in many application fields, with the rapid development of information technology, image recognition technology has also been very rapid development. In many social fields, the effective application of image recognition technology will enable the social and economic value to be given full play.