<title>Image processing and recognition using diffractive and digital techniques</title>

With the rapid development of technology, artificial intelligence is gradually applied in the field of image processing. Traditional image processing and recognition takes human extraction of features as a means to describe and characterize low-level edge information in images, but a large number of features extraction causes certain troubles to image recognition, and it is difficult to extract deep-level information. In recent years, deep learning, as an important emerging technology in the field of image recognition, has broad application prospects, and the research on image recognition technology has important theoretical value and practical significance to promote the development of computer vision and artificial intelligence. It opens up a whole new direction in image processing research, thus meeting the demand for efficient image processing. This paper reviews the applications of deep learning in image recognition, analyzes the classical convolutional neural network model, and summarizes the applications of deep learning in the fields of license plate recognition, face recognition, and medical image recognition.

Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This article mainly studies the forest image recognition system based on CCD and theodolite. In this article, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this article, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

The extraction of feature vectors of the images is the most important step in image recognition, because it allows a good description of the forms to be recognized. This step is an operation that makes it possible to convert an image into a vector of real or complex values that can serve as a signature for this image. For an accurate recognition system, the used feature vector must be invariant to the three image transformations (translation, rotation and scale), which means that, the descriptor vectors of the image and the transformed image by translation, rotation or scale must be equal. Given the importance of moments and their invariants in pattern recognition and imaging, based on Newton’s binomial and trinomial formulas and the normalized central moments, we construct in this paper four new series of moments for 2D and 3D image recognition, which are invariant to translation, scaling and rotation (TSR): the first is a set of non-orthogonal moment invariants for 2D images (2dMIs), the second is a set of orthogonal moment invariants for 2D images (2dOMIs), the third is a set of non-orthogonal moment invariants for 3D images (3dMIs) and the fourth is a set of orthogonal moment invariants for 3D images (3dOMIs). Using the proposed invariant moments (2dMIs), (2dOMIs), (3dMIs) and (3dOMIs), we construct two types of image descriptor vectors for 2D images and two types for 3D images, which are invariant to translation, rotation and scale. A series of experiments is performed to validate this new set of invariant moments and compare its performance with the existing invariants moments. The obtained results ensure the superiority of the proposed moments over all existing moments in image recognition. Experiments on processing time show that the proposed method is faster than the existing orthogonal invariant moments.

With the rapid development of digital image processing and recognition, the intelligent grid wins a great opportunity. We could be able to get the information from digital images quickly based on computer analysis, algorithms of deep learning and machine learning, grid data extraction, model training and image recognition. The article firstly reviewed the current status of traditional grid, including a broad overview of the specific strengths and weaknesses, secondly analyzed the intelligence algorithms of deep learning and machine learning based on neural network, then achieved the recognition and reconstruction of wiring diagram through image recognition based on neural network, lastly showed the prospects of image recognition in power system.

Text recognition in images is a very urgent problem in modern search engines. There are many different methods and techniques for text recognition. The paper is a method for text recognition in a standardized image. Standardized image means an image that has the same font, character size, certain writing order, such as the serial number or license plate of the car.In the paper, we developed an improved method for text recognition in the image. The method consists in a preliminary search of the same characters and memorizing their positions. Identical symbols are recognized only once. After recognition, symbols are arranged in the desired position. Image processing and isolation of character boundaries is performed using JavaCV.The modified method was developed based on the template method. Both methods were implemented in Java language. To create a text-recognition software, a neural network based on a single-layer perceptron was built. The results of tests have shown the superiority of the modified method compared to the original one. At best, the performance of the modified method is 300% of the performance of the original one. At worst, it is slower only by 5-10%. In addition, the modified algorithm requires 3 times fewer iterations.The modified algorithm allows to accelerate the text recognition process in standardized images if they have recurring characters.

In order to achieve intelligent, fast and accurate grading of apples, Apple's image acquisition and recognition system has been built, which is based on machine vision technology. Through color-to-gray, image noise reduction, image enhancement, and image segmentation processing of the collected image, three characteristics of the shape, size and color of the apple were recognized. Taking the 3 features of the apple identified from the image recognized as input, a multi-feature fusion classification method based on image processing and SVM was proposed in this paper. Through the constructed SVM vector machine, the apple's shape, size and color are synthesized to judge the apple's grade. The experimental results show that the classification method has the characteristics of high recognition rate and good real-time performance. This method lays the foundation for the application of machine vision technology in apple grading.

With the rapid development of urban rail transit in my country, the safety inspection of subway tunnel infrastructure is in urgent need of automated and intelligent theory and technology research and development. The subway tunnel lining environment is bad, there are a lot of noise and uneven illumination, and the texture features are complex. The traditional manual and image detection and recognition methods have gradually been replaced by more intelligent and effective deep learning algorithms. Therefore, this paper proposes a subway tunnel lining image recognition and detection algorithm that combines image processing and deep convolutional networks, which can quickly and accurately distinguish between cracked images and non-cracked images, and can realize intelligent target search and category labeling in complex images. In this experiment, a total of 4000 original images of subway tunnels were selected, and pixel-level shallow processing operations such as batch preprocessing, hierarchical feature analysis, and extraction of the circumscribed rectangle of the connected area were carried out. For the training sample library of crack images, pixel-level image processing experiments and classification and recognition experiments are carried out for the training sample library to obtain the optimal database processing results, and then the image recognition algorithm for complex cracks in subway tunnels is studied. Experiments show that there are more point noises and block noises in the results of the subway tunnel crack images, and the segmentation results of the crack images have relatively less noise residues. Due to the wall material, some cement materials are a little off, resulting in noise retention. This shows that the algorithm in this paper has a certain application value in the intelligent identification of actual subway tunnel diseases, and provides technical support and research ideas for the application of subsequent deep learning in subway tunnel disease detection.

Nowadays, occupational safety incidents have caused a large number of casualties during operation or maintenance in the factories. For example, it could happen during the compression process in the plastic injection molding industry that when a technician is in the middle of two molds to dismantle them and another personnel outside the work area does not pay special attention and accidentally presses the start button, the technical working between the molds may be instantly clamped by the molds, causing serious casualties. Also, we may easily bring about another kind of example in heavy industry: during the operation of a punching machine, the operator could be pressed by the punching die due to a momentary lapse of concentration, resulting in broken fingers or injuries to the arm, elbow, or wrist. Therefore, this study aims to apply image and speech recognition to the robotic arm so as to control the robotic arm by voice, or independently perform image recognition through verbal commands. In this way, the operator does not need to approach the machine. For instance, the voice-controlled robotic arm can perform image recognition to identify the type of object, and then the arm will pick it up and place it in the designated location. Additionally, the image recognition scheme can be enabled separately during the calibration stage to take pictures of the object to be recognized in the subsequent production process. In other words, it helps operators avoid accessing high-risk areas by employing verbal control for tasks that require human intervention. The simulation and actual test have evidenced that image recognition can be enabled independently by using speech recognition, and the robotic arm can be controlled directly by verbal commands to achieve remote control.

With the planning and implementation of national smart grid construction, on-line monitoring technology, as one of the key technologies of smart transmission, is also developing towards intelligence. With the construction of smart grid, the number of UHV long-distance and large-capacity transmission lines is increasing, and the load density of the grid is also increasing. As an important component of power system operation, the safety and reliability of power lines are very important. At the same time, with the popularization of helicopter inspection of power transmission lines, it not only provides opportunities but also challenges for automatic detection of defects in power transmission lines by image processing and recognition technology. How to effectively obtain basic information of power line operation from power line images obtained by helicopter inspection has become a research hotspot. Using automatic extraction method of image recognition can not only improve work efficiency and detection accuracy, reduce patrol personnel, but also overcome various defects of manual methods, and finally improve the safety of power line patrol and the accuracy of detection results.

In recent years, with the development of digital technology, digital image processing has been widely and deeply applied in the field of computer graphics. Digital image processing system is a complex real-time system, it from the camera, fax machine and other scanning equipment to obtain image information, after digital transformation, digital image information coding, filtering, enhancement, recovery, compression, storage and other processing, finally generate visual image. This design uses TMS320C6748 as the core processor of the system, SAA7113 as the video decoding chip of the system, CPLD as the sampling controller, DDR2 chip as the external expansion memory. The ROM expansion uses NAND flash memory chip. On the basis of hardware design, combined with software algorithm to complete image processing. The system can be used in information communication, image recognition, news scene and other fields of image processing and transmission. This paper analyzes the hardware structure and data processing algorithm of the system in detail. The experimental results show that the system can not only obtain higher compression ratio, but also reduce the distortion of the reconstructed image. The image processing system has certain practicability.

Interest in digital image processing techniques dates back to the early 1920's, when digitized pictures of world news events were first transmitted by submarine cable between New York and London. Applications of digital image processing concepts, however, did not become widespread until the middle 1960's, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth, having been a subject of interdisciplinary research in fields ranging from engineering and computer science to biology, chemistry, and medicine.

Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

Image recognition technology is an important branch of artificial intelligence research, using the computer to process, analyze and understand the image, in order to identify different patterns of objects. Image recognition technology is currently used in a wide range of applications, such as face recognition, fingerprint recognition, terrain survey, license plate recognition, etc. However, due to the possible existence of multiple categories in images, the blurring of graphic boundaries affects the results of image recognition. In this paper, we propose a multi-category multi-task image recognition method based on deep metric learning (MMDML). Specially, we combine triplet loss function and softmax loss function to construct the loss function, and take ResNet-50 network training to fit the optimal loss function for image recognition. To demonstrate the effectiveness of the proposed method, our method is compared with the other two methods on three common image recognition datasets, namely ImageNet, PASCALVOC, and Caltech. And the experimental results show that our algorithm has the highest Rank-1 and mAP on three datasets.

Digital filtering is essential for digital imaging, image recognition, and super-resolution technology. For example, the presence of noise in images captured by digital cameras causes deterioration of the image quality and image recognition rate. In order to improve the image recognition rate, noise reduction and edge preservation must be performed during preprocessing. Noise is generally reduced using low-pass filters, such as the Gaussian filter. Although they reduce noise, such filters also have the properties of blurring edge. A strong edge blur reduces the accuracy of the feature detection in image recognition. Therefore, in our previous study, a fast M-estimation Gaussian filter for images (FMGFI) was proposed as an image filter that simultaneously achieves denoising and edge preservation. In the FMGFI, the setting of the optimal basic width of the 2nd order B-spline basis functions is important for achieving simultaneous denoising and edge preservation. In this method, the optimal basic width of the FMGFI was determined not only by manually setting the basic width but also by human judgment of the filtered images. Consequently, the inability to automatically determine the optimal basic width hindered efficient denoising during image processing Therefore, in this research, we develop and propose a method that can automatically determine the optimal basic width of the FMGFI. The previously proposed method calculates using the same basic width for all the pixels over the entire image; in contrast, the proposed method calculates using the basic width automatically determined for each pixel. The experiments confirmed that the method proposed in this study achieves higher denoising and edge preservation performance than the ones used in previous research. The results also showed that it has the highest denoising performance against salt-and-pepper noise as compared to other filters: non-local mean filter, Gaussian filter, median filter, bilateral filter, adaptive bilateral filter, and FMGFI. The experimental results for the Gaussian noise sowed that the proposed method has the same denoising and edge preservation performance as the other filters in visual evaluation. From the above, the proposed method is expected to contribute to efficient denoising and improvement of image quality by using it as a preprocessing.

Learner attending auto-monitor in distance learning using image recognition and Bayesian Networks