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

This paper proposes a simple and fast digit recognition algorithm based on exterior contour features of a digital validation image. First, we convert the validation image into a binary one and divide the binary image into some single number image. Then compute the exterior contour features of the single image from top, bottom, left and right 4 directions. Finally, we recognize the number according to their contour features. The test results show that the recognition accuracy of this algorithm is relatively high and the speed is fast. In addition, it can be used to recognize other kinds of characters.

In recent years, with the popularity of the Internet, digital media has become an indispensable part of people's life. Image acquisition equipment is widely used in daily life and work. However, due to technical problems, it can not meet the performance requirements of users. At the same time, a large number of data and information are difficult to be effectively processed, which also reduces the image quality, reduces the storage space and increases the image distortion. Therefore, it is necessary to develop new algorithms to improve these defects. Therefore, this paper designs and develops an automatic digital media image recognition system based on mean clustering algorithm. Firstly, this paper describes the concept and characteristics of digital media. Then, this paper also studies the application of mean clustering algorithm. Based on this, an automatic digital media image recognition system is designed, and the performance of the system is tested by simulation. Finally, the test results show that the algorithm can accurately identify media images. In general, the performance of the digital media image automatic recognition system based on mean clustering algorithm can meet the needs of target users.

Based on chaos synchronization of two response lasers (RLs) injected by the common chaos signals from a driving laser (DL) with double optical feedback (DOF) and an additional neural network (NN), a bidirectional chaotic secure communication system with machine learning capability is proposed and the long-distance digital image transmission and recognition performances are numerically investigated. The simulated results show that the time delay signature (TDS) of the driving chaotic signals from DL with DOF is greatly weakened under suitable operation conditions. Two RLs can generate chaos signals with low TDS and broad bandwidth, and the high-quality chaos synchronization between two RLs can be achieved while the synchronization quality between DL and any one of RLs is extremely bad, which is helpful to realize the secure communication. Using the synchronized chaos output signals from two RLs as chaos carriers and chaos masking (CM) encoding method, the bidirectional 10 Gb/s secure communication over 140 km fiber channel can be realized and the effects of the fiber parameters on the communication performances are discussed. After adopting an additional NN, a long-distance digital image transmission and recognition based on this proposed secure communication system can be realized and the communication distance can be extended to 220 km.

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.

This paper investigates a fractionation method of cellulosic fibres in dry state. Although processing of cellulosic fibres in airstream has been utilised for decades in airlaid processes, no attempts to fractionate fibres in dry state have been reported. Our main goal is to determine how fibre properties and fibre consistency affect fibre flocculation and thus the behaviour of fibres in fractionation. Also evaluations of quality and capacity of fractionation operation are made. Pulp flocculation behaviour is measured with digital imaging and image recognition technique before and after the fractionation device. A doubleview, orthogonal imaging approach is applied obtaining statistics of three-dimensional shape, dimensions and coordinates of detected fibre flocs. Fraction and fibre properties are determined with Metso Fractionator and Metso Fiberlab. Results show that fibre behaviour in a given system strongly depends on the pulp properties. Flocculation was found to be strongly dependent on fibre consistency. Evaluation of fractionation operation was done with help of calculations of mass-reject rates and separation efficiencies. Calculations showed that highest separation efficiencies can be achieved with system when mass-reject rate is between 0.30 and 0.40.

This paper describes a new methodology and associated theoretical analysis for rapid and accurate extraction of level sets of a multivariate function from noisy data. The identification of the boundaries of such sets is an important theoretical problem with applications for digital elevation maps, medical imaging, and pattern recognition. This problem is significantly different from classical segmentation because level-set boundaries may not correspond to singularities or edges in the underlying function; as a result, segmentation methods which rely upon detecting boundaries would be potentially ineffective in this regime. This issue is addressed in this paper through a novel error metric sensitive to both the error in the location of the level-set estimate and the deviation of the function from the critical level. Hoeffding's inequality is used to derive a novel regularization term that is distinctly different from regularization methods used in conventional image denoising settings. Building upon this foundation, it is possible to derive error performance bounds for the proposed estimator and demonstrate that it exhibits near minimax optimal error decay rates for large classes of level-set problems. The proposed method automatically adapts to the spatially varying regularity of both the boundary of the level set and the underlying function.

We present a classification algorithm based on ant colony system(ACS) for classifying digital images. The ACS has been recently emerged as a useful tool for the pattern recognition, image extraction, and edge detection. The classification algorithm of digital images is very important in the application areas of digital image coding, image analysis, and image recognition because it significantly influences the quality of images. The conventional procedures usually classify digital images with the fixed value for the associated parameters and it requires postprocessing. However, the proposed algorithm utilizing randomness of ants yields the stable and enhanced images even for processing the rapidly changing images. It is also expected that, due to this stability and flexibility of the present procedure, the digital images are stably classified for processing images with various noises and error signals arising from processing of the drastically fast moving images could be automatically compensated and minimized.

Classification of sugar using digital imaging and pattern recognition techniques

The visual representation of emotion in image technology is particularly important in today's society. Visual perception, which is mainly based on the changes in the audience's psychological emotions, has become a thinking derivative of digital image technology, and further technical implementation of image transformation, adjustment, repair, reconstruction, replacement, etc. Ingesting the characteristics of emotional appeal is the main direction of this research. With the continuous development of blockchain, virtual and information technology, the research on the content and emotion of digital images has become a hot spot in the design of visual reconstruction. A multi-dimensional computer-generated language spanning space is proposed to improve the methods of digital image acquisition, recognition, transformation and segmentation. Reconstruction design research on the semantic characteristics of computer-generated digital images and content emotional representations. Based on the multi-features of digital images, the differences in image shape, texture, color, motion and spatial domain features are analyzed, and the content emotion and semantic reconstruction of digital images are distinguished. In information transmission, multi-dimensional space capture, output, and quantitative analysis methods are used to establish a new sample arrangement matrix of digital images, select the best parameters for content expression, and reconstruct and process quasi-vision, combined with parameters to realize emotion recognition of digital images. The detection coefficient and dimension are determined by simulation and virtual technology, and the high recognition rate of digital image is obtained, and a good digital image technology display effect is achieved.

This paper explores teenage girls' engagement with digital images on social media. Using new feminist materialism, we foreground digital images as an assemblage of materialities (human and more‐than‐human) filled with affective potentials that materialise in/capacities. Drawing from interviews and focus group discussions, we show how the production and sharing of selfies through posting and sexting unlocked new ‘becomings’ through expressions of heterosexual desirability and pleasure but also generated fear through sexual objectification, sexual double standards and harassment. A recognition of digital images as materially embodied through which unequal gender power relations materialise is vital to addressing online sexual risk.

Use of digital images has increased tremendously in medical science and with that has increased the query on authenticity of the image. Authenticity of the digital image is very important in the area of scientific research, forensic investigations, government documents, etc. With the help of powerful and user friendly image editing software like Microsoft Paint and Photoshop it became extremely easy to tamper with a digital image for malicious objective. Of late this problem has been encountered in medical imaging also for the purpose of fake insurance claims. We propose an algorithm to address this problem by which one can detect and localize tampering in a digital medical image. This algorithm is based on hash based representation of such image and uses discrete wavelet transform method to carry out detection and localization of tampering. We will show that our algorithm is robust against harmless manipulation and sensitive enough for even a minute tampering. Our proposed technique consumes less resource as it works with smaller hash function in comparison with the similar available techniques.

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.

— This study evaluates the efficacy of the Tesseract Optical Character Recognition (OCR) system through a practical application lens. Tesseract, an open-source OCR tool, is widely recognized for its adaptability and broad usage across various digital imaging and text recognition domains. This paper explores Tesseract's performance in converting scanned documents into editable text formats, emphasizing its accuracy, efficiency, and usability in diverse scenarios, including complex document layouts and varied text quality. By conducting systematic tests across multiple data sets, including printed and handwritten texts, the study provides quantitative and qualitative assessments of Tesseract's capabilities. Additionally, comparative analysis with other leading OCR tools offers a comprehensive understanding of Tesseract's positioning in the OCR landscape. The results indicate that while Tesseract performs robustly in standard text recognition tasks, challenges remain in handling intricately styled fonts and backgrounds. The paper concludes with suggestions for potential improvements and areas for future research, aiming to enhance the practical applications of OCR technology.

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.