Image recognition is an important technology of the information age, which was created to allow computers to replace human beings with a large amount of physical information. With the development of computer technology, humans are becoming more aware of image recognition techniques. The process of image recognition technology is divided into access to information, pre-processing, feature extraction and selection, design and classification decisions. The introduction of image identification techniques, their technical rationale and mode recognition were briefly analysed, followed by the introduction of image identification techniques and the application of non-linear downscaling image identification techniques and image identification techniques for the neural network. From this it can be summarized that the use of image-processing techniques is widespread, that human life will not be free from image-recognition techniques, and that research into image-identification techniques will be of great importance。
Introduction of image recognition techniques
Image recognition is an important area of artificial intelligence. The development of image identification went through three stages: text recognition, digital image processing and identification, and object identification. Image recognition, by definition, is the processing, analysis of images, and ultimately the identification of what we are looking at. The image recognition that we are referring to today is not just the eye of human beings, but is based on computer technology. Although human identification is powerful, for a rapidly developing society, the ability of humans to identify themselves is no longer sufficient to meet our needs, and computer-based image identification techniques have emerged. It is like humans studying biological cells, and it is unrealistic to observe cells solely on the naked eye, so naturally, microscopes and other instruments for accurate observations are created. Often, new technologies emerge when there is an inherent need for technology that cannot be addressed. The same is true of image recognition techniques, which are created to allow computers to replace human beings in processing large quantities of physical information that cannot be identified or that have a particularly low degree of recognition。
1. 1 technical principles for image identification
In fact, the rationale behind the image recognition technique is not difficult, but the information it is about to process is cumbersome. Any processing techniques for computers are not created in a vacuum; they are inspired by life practice and are modelled by the programme. Computer image recognition techniques and human image recognition are not fundamentally different in principle, except that machines lack the influence of human perception and vision. Human image recognition is not based solely on memory stored in the mind of the entire image. We identify images by categorising them by their own characteristics, and then by identifying them by the characteristics of each category, but we are not aware of this in many cases. When we look at a picture, our brain quickly senses whether we have seen it or whether it's similar. Indeed, in the middle of “seeing” and “seeing”, there was a rapid identification process, which was somewhat similar to the search. In the process, our brains are identified according to the categories already identified in the memory, to see if there are memory storages with the same or similar characteristics as the image, so that we can see if the image has been seen. The same is true of image recognition techniques in machines, which exclude redundant information by classifying and extracting important features. These characteristics extracted by the machine are sometimes very visible and sometimes very common, which greatly affects the rate of machine identification. In short, in the visual recognition of computers, the content of the images is usually described using image features。
1. 2 mode recognition
Model recognition is an important component of artificial intelligence and information science. Model recognition refers to the process of analysing and processing different forms of information that represent an object or phenomenon so as to obtain a description, identification and classification of an object or phenomenon。
The image recognition technique of computers is to simulate the image recognition process of humans. Model recognition during image identification is essential. Model recognition is a fundamental intelligence of human beings. But with the development of computers and the emergence of artificial intelligence, humans themselves are unable to identify their own models to meet the needs of life, and humans want computers to replace or expand part of their intellectual labour. So the model recognition of the computer is created. In short, model recognition is the classification of data, a science closely integrated with mathematics, where most of the ideas used are probability and statistics. Model recognition is divided into three main types: statistical model recognition, meta-model recognition, and vague model identification。
2 image identification technology process
Since the image recognition techniques of computers are the same as those of humans, their processes are similar. The process of image recognition technology is divided into the following steps: information acquisition, pre-processing, feature extraction and selection, design and classification decision-making。
Access to information means the conversion of information such as light or sound into electrical information through a sensor. That is to acquire the basic information of the subject and to transform it into something that the machine can understand。
Pre-processing refers primarily to noise removal, smoothing, transformation, etc. In image processing, thereby enhancing the important features of the image。
Characteristic extraction and selection refers to the need for character extraction and selection in mode recognition. The simple understanding is that the images that we study are diverse, and if they are to be distinguished by some method, they are to be identified through their own characteristics, which are acquired through their extraction. The characteristics obtained during the character extraction may not all be useful for this identification, at which point useful features will be extracted, that is, the choice of characteristics. The extraction and selection of features is one of the key techniques in the image identification process, so the focus of image identification is understood to be this step。
Catalogue design refers to training leading to an identification rule through which a feature classification can be obtained, allowing image identification techniques to achieve a high recognition rate. Classification decisions refer to the classification of identified objects in the characteristic space, thereby better identifying the specific category of objects studied。
3 analysis of image identification techniques
With the rapid development of computer technology and the continuous advances in technology, image recognition techniques have been applied in many areas. On february 15, 2015, new wave technology released a news release: “microsoft recently published a research paper on image recognition, which in a benchmarking test for image recognition has exceeded human recognition capabilities. The image recognition error rate for humans in the classification database image net is 5. 1 per cent, while the microsoft study group's in-depth learning system can achieve a 4. 94 per cent error rate.” from this news, we can see that there is a tendency for image recognition techniques to go beyond human image recognition. This also shows that future image identification techniques have greater research relevance and potential. Moreover, computers do in many ways have advantages beyond human reach, and that is why image recognition techniques can bring more applications to human society。
3. 1 image recognition techniques for neural networks
Neural network image recognition is a relatively new type of image identification technique, a method of image recognition that integrates neuronetwork algorithms based on traditional image identification methods. Here, the neural network is the artificial neural network, which means that the neural network is not the real neural network of animals themselves, but is artificially produced by humans imitating the animal neural network. In neuronet image recognition techniques, the neural network image recognition model, which integrates genetic algorithms with the bp network, is very classic and is applied in many areas. Using a neural network system in the image recognition system, the characteristics of the images are generally extracted first, and then the characteristics of the images are mapped to the neural network for image recognition classification. In the case of auto-identifying vehicles, for example, when the vehicle passes, the detection equipment that the vehicle owns is sensitive. At this point, the detection device activates the image acquisition device to capture images on the positive and negative sides of the vehicle. Upon acquisition, the image must be uploaded to the computer for preservation for identification. Finally, the license plate locator module extracts the license plate information, identifies the characters on the plate and shows the final results. A template-based matching algorithm and an artificial neural network algorithm were used to identify the characters on the licence plate。
3. 2 non-linear downscaling image identification techniques
Computer image recognition is an exceptionally high-level identification technique. Regardless of the resolution of the images themselves, the data they generate are often multidimensional, which poses great difficulties for computer identification. If computers are to be able to identify efficiently, the most immediate and effective way to do so is to de-grade. Declines are divided into linear downscaling of peacekeeping and non-linear downscaling. Examples such as the main component analysis (pca) and the linear odd analysis (lda) are common linear downscaling methods, characterized by simplicity and ease of understanding. But it is the aggregate data set that is processed by linear downsizing, and it is the optimal low-dimensional projection of the entire data set. This linear downscaling strategy, which is certified to be highly complex and to take relatively large amounts of time and space, has resulted in image recognition techniques based on non-linear downscaling, a highly effective method of extraction of non-linear features. This technique can detect the non-linear structure of the image and can reduce it without destroying its intrinsic structure, allowing the image recognition of the computer to take place at the lowest possible dimension, thus increasing the rate of recognition. For example, the dimensions required for a human face image recognition system are often very high, and the complexity is undoubtedly a huge “disaster” for computers. The uneven distribution of human face images in high-dimensional space has made it possible for humans to gain access to compact human face images through non-linear downscaling techniques, thus increasing the efficiency of human face recognition techniques。
3. 3 application of image identification techniques and prospects
Computer image recognition techniques are applied in many areas, including public safety, biology, industry, agriculture, transport and medical care. Examples include the system of identification of licence plates in transport; human face identification techniques in public safety; fingerprint identification techniques; seed identification techniques in agriculture; food quality testing techniques; and electrocardiogram identification techniques in medicine. As computer technology continues to evolve, image recognition techniques are being optimized and algorithms are being improved. Images are the main source of human access and exchange of information, so image identification techniques associated with images must also be the focus of future research. In future, the image recognition technology of the computer is likely to become more visible in more areas, and its applications are open-ended and human life will become even more inseparable。
While image identification techniques are emerging, their applications are already quite extensive. Moreover, image recognition technology is growing and, as technology advances, humans will become more aware of it. The future of image recognition will be stronger and more intelligent in our lives, bringing significant applications to more areas of human society. In this age of informatization in the twenty-first century, we cannot imagine what our lives would have become if we had left the image recognition technology. Image recognition is an essential technology for present and future human life。
The future development of artificial intelligence, big data, cloud computing and the networking of goods are noteworthy. They are front-line industries, and the intellectual age focuses on the introduction and spectra of artificial intelligence and big data
Ai can identify the image, but does it understand the title
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What's the image recognition technique? What's it for
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Multi-intellectual age - an introduction to artificial intelligence and big data learning
