Image recognition is based on what the image is

Image recognition is based on the main features of the image. Each image has its characteristics. Research on eye movements during image recognition shows that the line of sight is always focused on the main features of the image, that is, on the places where the image contour has the greatest curvature or where the direction of the contour changes suddenly. The amount of information is the largest; and the scanning route of the eyes always turns from one feature to another in sequence.

The operating environment of this tutorial: Windows 7 system, Dell G3 computer.

Image recognition technology is an important field of artificial intelligence. It refers to the technology of object recognition on images to identify targets and objects of various different modes.

The development of image recognition has gone through three stages: text recognition, digital image processing and recognition, and object recognition. Image recognition, as the name suggests, is to perform various processing and analysis on images, and finally identify the target we want to study. The image recognition referred to today is not just the use of human eyes, but recognition with the help of computer technology.

Principles of image recognition technology

There is no essential difference in principle between computer image recognition technology and human image recognition. Recognition relies on the classification of the inherent characteristics of the image, and then identifies the image through the characteristics of each category. When we see a picture, our brain will quickly sense whether we have seen this picture or something similar to it. picture.

In this process, our brain will identify according to the categories that have been classified in the stored memory to see if there is a stored memory with the same or similar characteristics as the image, thereby identifying whether the image has been seen. .

#Image recognition technology may be based on the main features of the image. Each image has its characteristics, such as the letter A has a tip, P has a circle, and the center of Y has an acute angle, etc. Research on eye movements during image recognition shows that the line of sight is always focused on the main features of the image, that is, on the places where the curvature of the image contour is the largest or the direction of the contour changes suddenly, where the amount of information is the largest. Moreover, the scanning path of the eye always switches from one feature to another in sequence. It can be seen that in the image recognition process, the perceptual mechanism must eliminate the input redundant information and extract the key information. At the same time, there must be a mechanism in the brain responsible for integrating information, which can organize the information obtained in stages into a complete perceptual image.

Pattern recognition is an important part of artificial intelligence and information science. Pattern recognition refers to the process of analyzing and processing different forms of information representing things or phenomena to obtain a description, identification and classification of things or phenomena.

Image recognition technology is based on the main features of the image. Each image has its characteristics. Research on eye movements during image recognition shows that the line of sight is always focused on the main features of the image, that is, on the places where the image contour has the greatest curvature or where the direction of the contour changes suddenly. The largest amount of information. Moreover, the scanning path of the eye always switches from one feature to another in sequence. It can be seen that in the image recognition process, the perceptual mechanism must eliminate the input redundant information and extract the key information. At the same time, there must be a mechanism in the brain responsible for integrating information, which can organize the information obtained in stages into a complete perceptual image.

The process of image recognition technology

Since computer image recognition technology has the same principles as human image recognition, their processes are also similar. . The process of image recognition technology is divided into the following steps: information acquisition, preprocessing, feature extraction and selection, classifier design and classification decision-making.

The acquisition of information refers to converting information such as light or sound into electrical information through sensors. That is to obtain the basic information of the research object and transform it into information that the machine can understand through some method.

Preprocessing mainly refers to operations such as denoising, smoothing, and transformation in image processing, thereby enhancing the important features of the image.

Feature extraction and selection refers to the need to extract and select features in pattern recognition. The simple understanding is that the images we study are of various kinds. If we want to use some method to distinguish them, we must identify them through their own characteristics, and the process of obtaining these characteristics is feature extraction.

The features obtained in feature extraction may not all be useful for this recognition. At this time, useful features must be extracted. This is feature selection. Feature extraction and selection are one of the most critical technologies in the image recognition process, so understanding this step is the focus of image recognition.

What are the applications of image recognition technology?

Image recognition is divided into biometric recognition, object and scene recognition and video recognition. Biometric recognition includes fingerprints, palm shape, eyes (retina and iris), face shape, etc.; object and scene recognition includes: signature, voice, walking gait, keyboard typing strength, etc.

Image recognition is a comprehensive problem, covering image matching, image classification, image retrieval, face detection, pedestrian detection and other technologies, and is used in Internet search engines, autonomous driving, medical analysis, remote sensing analysis and other fields It has wide application value.

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