Process II of hottest image recognition and proces

Image recognition and processing process II

● image recognition

image recognition is to classify the above processed images and determine the category name. It can select the features to be extracted on the basis of segmentation, measure some parameters, extract these features, and finally classify them according to the measurement results. In order to better recognize the image, we also need to analyze the structure of the whole image, describe the image, so as to get an explanation and understanding of the main information of the image, and deepen the understanding of the image through the structural relationship between many objects, so as to better help identify. Therefore, image recognition is to find out its shape, texture and other features in each part after segmentation, that is, feature extraction (sometimes including image segmentation), so as to classify the image and analyze the structure of the whole image. For the image recognition link, the input is the image (generally the image processed above), the output is the category and the structural analysis of the image, see Figure 1 (b), and the result of the structural analysis is to describe the image, so as to get an understanding and interpretation of the important information of the image

it should be noted here that image segmentation is not necessarily carried out completely during image processing. For some problems, segmentation and recognition are carried out at the same time, such as the sorting and grading of mechanical parts. Therefore, image processing and image recognition intersect with each other

● image understanding

the so-called image understanding is a general term. The ultimate purpose of the above image processing and image recognition is to describe and explain the image, so as to finally understand what image it is. It is based on image processing and image recognition, and then makes structural syntactic analysis according to classification to describe and interpret images. Therefore, image understanding includes image processing, image recognition and structure analysis. For the understanding part, the input is the image, and the output is the description and interpretation of the image, as shown in Figure 1 (c)

in essence, image understanding belongs to the category of artificial intelligence. Image understanding also requires image processing, recognition and structure analysis. For example, computer chess, we need to do these jobs. First of all, we should store human wisdom in the computer. As much wisdom as we teach it, it will have as much wisdom. This is inherent in the machine, but after receiving a part of "wisdom", the computer can analyze and infer according to logical reasoning

● the relationship between image recognition, image processing and image understanding

as mentioned above, image understanding is a general term, it is a system. Each part has a certain relationship with the previous part, which can also be said to have a feedback function. For example, segmentation can be carried out in preprocessing, and the system is not isolated. In order to play its function, it needs necessary information from the outside all the time, so that each part can work effectively. These external information refers to the views, assumptions, methods, etc. of processing and solving problems. For example, according to the actual image, what preprocessing needs to be used in the processing part, how to segment, what features to extract and how to extract features in the recognition part, how to classify, how many categories to be divided, and finally provide the structural information required for structural analysis, etc.

in this system, "preprocessing" "Segmentation" can generally be regarded as image processing. Because of its uniform hierarchical structure, "feature extraction" and "classification" belong to image recognition; The content of syntactic image recognition is from recognition to structure analysis. The result of the whole system is image description and interpretation. When a new object (image) is sent into the system, it can be explained to explain what it is

● technical characteristics of image recognition

before the emergence of computer processing, image processing was analog processing such as optical photographic processing and video signal processing. Compared with analog processing, digital image processing technology is excellent in flexibility, accuracy, adjustment and reproducibility, in addition to the large processing speed and memory requirements. It can freely process various processes with programs, and can achieve high accuracy. This is indeed an advantage compared with the substantial improvement of the device in order to improve the accuracy of an order of magnitude in simulation processing. In addition, due to the continuous progress of semiconductor technology, the special high-speed processor for image processing has been developed, and the image display based on IC memory has also reached a feasible level, which further accelerates the development and practicality of digital image processing technology

in order to deal with the computer, Professor Cheng said: "In this article, the image must be expressed in numerical value. The digital image is the gray distribution on the two-dimensional plane. The digital image information has the following characteristics:

1. The amount of information is large. For example, a TV image is composed of 512 x 512 pixels. If the gray level is expressed in the 8-bit binary system, there are 28=256 gray levels, then the amount of information of a frame image is 512 x 512 x 8=2 097152bit, for such a large amount of information Only a computer can do the image processing of, and the computer has a large amount of memory

2. The frequency band occupied by digital image is wide. Compared with voice information, the frequency band occupied by digital image is several orders of magnitude larger. For example, the bandwidth of TV image is 5mhz-6mhz, while the bandwidth of voice is only about 4kHz. The wider the frequency band is, the more difficult it is to realize the technology, and the cost accuracy can meet the requirements, which puts forward higher requirements for the frequency band compression technology

3. each pixel in the digital image is not independent, and its correlation is very large. For example, in a TV picture, the correlation coefficient of two adjacent pixels in the same row or the pixels between two adjacent rows can reach 0.9, and the correlation between two adjacent frames is larger than the intra frame correlation. Therefore, image information compression has great potential

4. the processed digital image needs to be observed and evaluated by people, so it is greatly affected by human factors. Because the human visual system is very complex, it is greatly affected by environmental conditions, visual performance and human subjective consciousness, so it requires a good cooperation between the system and people, which is still a great research topic

modern automatic recognition technology and Application