Analysis of image processing difficulties and processor selection

Image processing generally refers to digital image processing. Digital image refers to a large two-dimensional array obtained by sampling and digitizing with digital cameras, scanners, and other equipment. The elements of this array are called pixels, and their values ​​are integers, called gray values. The main contents of image processing technology include image compression, enhancement and restoration, matching, description and recognition.

With the improvement of people's quality of life requirements, the global trend of counter-terrorism, and the continuous advancement of digital technology itself, biometric solutions and video surveillance solutions relying on fingerprint recognition, iris recognition, face recognition and other technologies are gradually becoming more and more Important means of personal, family, business and social security. The biometrics scheme mainly includes four steps: image acquisition, image preprocessing, feature sampling, and matching analysis; and the video surveillance scheme mainly includes image acquisition, image preprocessing, image processing and transmission, image display, and image management. It is not difficult to see that whether it is biometrics or video surveillance, image preprocessing is necessary. In fact, the flexibility, complexity, occupancy of image processing chip resources, and the length of processing time of the image preprocessing algorithm will have a significant impact on the operation of the entire system. Therefore, image preprocessing is a difficult and critical task for the entire security program, which directly determines the accuracy and convenience of subsequent image processing and analysis.

Image analysis

Extract some useful metrics, data or information from the image. The purpose is to get some numerical result, not to produce another image. The content of image analysis overlaps with the research fields of pattern recognition and artificial intelligence, but image analysis is different from typical pattern recognition. Image analysis is not limited to classifying specific areas in an image according to a fixed number of categories, it mainly provides a description of the image being analyzed. To this end, it is necessary to use both pattern recognition technology and knowledge base on image content, that is, the content of knowledge expression in artificial intelligence. Image analysis needs to use image segmentation to extract the features of the image, and then describe the image symbolically. This description can not only answer whether there is a specific object in the image, but also make a detailed description of the image content.

The various contents of image processing are related to each other. A practical image processing system often combines several image processing techniques to obtain the desired results. Image digitization is the first step in transforming an image into a form suitable for computer processing. Image coding techniques can be used to transmit and store images. Image enhancement and restoration may be the final purpose of image processing, or it may be preparation for further processing. The image features obtained by image segmentation can be used as the final result or as the basis for the next image analysis.

Image matching, description, and recognition compare and register images, extract the features and interrelationships of images through a fractional system, get a symbolic description of the image, and compare it with the model to determine its classification. Image matching attempts to establish the geometric correspondence between two pictures and measure the degree of similarity or difference. Matching is used for registration between pictures or between pictures and maps, for example to detect changes in scenes between pictures taken at different times and find out the trajectory of moving objects.

Extracting some useful metrics, data, or information from an image is called image analysis. The basic step of image analysis is to divide the image into non-overlapping areas, each area is a continuous set of pixels, measure their nature and relationship, and finally compare the resulting image relationship structure with the model describing the scene classification. To determine its type. The basis of recognition or classification is the similarity of images. A simple similarity can be defined by the distance in the regional feature space. Another similarity measure based on pixel values ​​is the correlation of image functions. The last similarity defined in relational structure is called structural similarity.

Segmentation, description and recognition for image analysis and understanding will be used in various automated systems, such as character and pattern recognition, product assembly and inspection with robots, automatic military target recognition and tracking, fingerprint recognition, X-ray photos And automatic processing of blood samples. In such applications, technologies such as pattern recognition and computer vision are often required to be integrated, and image processing appears more as pre-processing. The rise of multimedia applications has greatly promoted the application of image compression technology. Images, including dynamic images such as video tapes, will be converted into digital images, and stored in the computer together with text, sound, and graphics, and displayed on the computer screen. Its application will expand into new areas such as education, training and entertainment.

Image preprocessing analysis

According to different purposes, image preprocessing can be divided into clear processing of the collected image, preprocessing of the image before recognition, and preprocessing of the image before compression, etc. Among them, clearing the collected image mainly includes subsequent correction of the inconsistency of the photosensitive unit of the CMOS or CCD image sensor, compensation for the difference between the actual environment and the image collected by the sensor (such as backlighting), and removal of the original image collected Noise processing, etc. Although this kind of preprocessing algorithm itself is not difficult, but with the popularity of real-time requirements, especially when the pixels are large, this algorithm still puts high requirements on the processing power of DSP.

The pre-processing before image recognition is very purposeful, and may need to destroy the original pixels and distribution for subsequent feature extraction. The difficulty of this preprocessing algorithm varies depending on the recognition occasion. To integrate the latter part of the recognition algorithm, select the appropriate DSP. The pre-processing before image compression mainly refers to changing YUV422 to YUV420, RGB to YUV, etc. This type of processing often has real-time requirements. If it is implemented in software, it will have higher requirements for processing performance; if it is implemented in hardware, although the processing performance is guaranteed, the hardware cost will increase.

At the same time, according to different applications, image preprocessing can be divided into image preprocessing in biometric applications and image preprocessing in video surveillance applications. For biometric applications, taking fingerprint recognition as an example, the pre-processing mainly includes fingerprint image enhancement, fingerprint image binarization, fingerprint image thinning, and fingerprint image thinning post-processing. The image preprocessing in video surveillance applications mainly refers to the analysis of continuous images output by the image sensor to obtain sufficient information, and through automatic white balance, gamma correction, auto focus, auto exposure, backlight compensation, etc. Improve the actual effect of the image.

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