What classification does image processing belong to?
Question 2: Regarding image processing, is the classification of images the same as its discrimination? Image classification is a concept of image pattern recognition. For example, this technology will be used to detect faces in pictures. Simply put, a block in an image is divided into two categories, namely, human face and non-human face.
The degree of discrimination of image classification is related to the specific image classifier.
Question 3: What is the main content of digital image processing? The main research contents of digital image processing are as follows: 1) Image transformation is directly processed in the spatial domain because of the large image array, which involves a lot of calculation. Therefore, various image transformation methods, such as Fourier transform, Walsh transform, discrete cosine transform and other indirect processing techniques, are often used to transform spatial domain processing into transform domain processing, which can not only reduce the computational complexity, but also obtain more effective processing (for example, Fourier transform can carry out digital filtering in frequency domain). At present, the newly developed wavelet transform has good localization characteristics in both time domain and frequency domain, and has also been widely and effectively applied in image processing. 2) Image coding compression Image coding compression technology can reduce the amount of data (that is, the number of bits) describing the image, thus saving image transmission and processing time and reducing the occupied memory capacity. Compression can be achieved without distortion, or it can be carried out under allowable distortion conditions. Coding is the most important method in compression technology and the earliest and most mature technology in image processing technology. 3) Image enhancement and restoration The purpose of image enhancement and restoration is to improve image quality, such as removing noise and improving image clarity. Image enhancement does not consider the cause of image degradation, but highlights the interested parts of the image. If the high frequency component of the image is enhanced, the outline of the object in the image can be clear and the details can be obvious; For example, enhancing low-frequency components can reduce the influence of noise in the image. Image restoration requires a certain understanding of the causes of image degradation. Generally speaking, the degradation model should be established according to the degradation process, and then some filtering method should be used to restore or reconstruct the original image. 4) Image segmentation Image segmentation is one of the key technologies in digital image processing. Image segmentation is to extract meaningful features from the image, and its meaningful features include edges and regions in the image, which is the basis for further image recognition, analysis and understanding. Although many methods of edge extraction and region segmentation have been developed, there is not an effective method that can be universally applied to all types of images. Therefore, the research on image segmentation is still deepening, which is one of the hot spots in image processing. 5) Image description is a necessary prerequisite for image recognition and understanding. As the simplest binary image, its geometric features can be used to describe the characteristics of objects, while the general image description methods adopt two-dimensional shape description, including boundary description and region description. Two-dimensional texture features can be used to describe special texture images. With the in-depth development of image processing research, the description of three-dimensional objects is studied, and methods such as volume description, surface description and generalized cylindrical description are proposed. 6) Image classification (recognition) Image classification (recognition) belongs to the category of pattern recognition, and its main content is to segment and extract features of images after certain preprocessing (enhancement, restoration and compression), so as to make decision classification. Classical pattern recognition methods are often used in image classification, including statistical pattern classification and syntactic (structural) pattern classification. In recent years, the newly developed fuzzy pattern recognition and artificial neural network pattern classification have attracted more and more attention in image recognition.
Question 4: What are the characteristics of common image types in digital image processing? Compared with optical and other analog methods, digital image processing has the following remarkable characteristics: 1 has the characteristics of digital signal processing technology. (1) has high processing accuracy. (2) Good reproducibility. (3) High flexibility. 2. The image after digital image processing is for people to observe and evaluate, or it may be the preprocessing result of machine vision.
Question 5: What does image processing mean? Do you mean ps? Is to use a computer to process a series of images to achieve the desired results. Ps is just one of them. There are many image processing software, such as Meitu Xiu Xiu, which is difficult to answer. Hope to adopt.
Question 6: What is the difference between "image processing" and "remote sensing image processing and recognition"? Remote sensing image processing and recognition is a research direction of image processing. The difference between them is that image processing mainly involves computer algorithms to store and analyze images (photoshop is a very typical image processing software), while remote sensing image processing is more inclined to extract useful information from remote sensing images by using the knowledge of different disciplines. For example, in order to obtain quantitative information of dust weather from remote sensing images, it is not enough to know only the knowledge of image processing, but also the atmospheric knowledge involved in this process, such as brightness temperature and aerosol optical thickness.
As far as I know, pattern recognition can be classified into the category of remote sensing image processing here. In the field of remote sensing, pattern recognition can be regarded as a method to extract remote sensing thematic information. The most familiar example is the use of supervised or unsupervised classification to obtain land use information. This information extraction process can be regarded as a workflow of remote sensing image processing, so pattern recognition belongs to remote sensing image processing.
It is difficult to generalize the employment problem. After all, everyone's situation is different and their choices are different. At the same time, there are different opinions on the Internet. But at present, some research institutes or similar information centers are better choices.
Good luck.
Question 7: What two types of images are digital image processing technology mainly aimed at? It should be a gray image and a binary image.
Most of the functions of image processing technology are to process gray images and binary images. There is almost no processing function for color images.
Question 8: The difference between image processing and computer vision Hello! Image processing and computer vision are closely related, so you may try these two keywords when you search for technical articles. The difference between them is that image processing focuses on "processing" images, such as enhancement, restoration, denoising, segmentation, etc. Computer vision uses computers (perhaps moving) to simulate human vision, so simulation is the ultimate goal in the field of computer vision. To achieve this goal, at least two things should be done, the first is image processing, and the second is image understanding. For example, the data read by robot eyes may be fuzzy or noisy, so it is necessary to denoise and restore first. After that, if the robot can understand what this image means, such as a specific military target, then it may have to segment it and then carry out pattern recognition in a statistical way. Obviously, recognizing this part belongs to image understanding, not just image processing.
Image processing mathematicians can do it, but mathematicians can't do computer vision, which is always an engineer's business.