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The depth synthesis of image texture is neglected in the current image visual communication technology, which leads to the poor visual effect. Therefore, the design method of film and TV animation based on 3D visual communication technology is proposed. Collect film and television animation videos through 3D visual communication content production, server processing, and client processing. Through stitching, projection mapping, and animation video image frame texture synthesis, 3D vision conveys animation video image projection. In order to ensure the continuous variation of scaling factors between adjacent triangles of animation and video images, the scaling factor field is constructed. Deep learning is used to extract the deep features and to reconstruct the multiframe animated and animated video images based on visual communication. Based on this, the frame feature of video image under gray projection is identified and extracted, and the animation design based on 3D visual communication technology is completed. Experimental results show that the proposed method can enhance the visual transmission of animation video images significantly and can achieve high-precision reconstruction of video images in a short time. [ABSTRACT FROM AUTHOR]

In order to solve the problems of poor region delineation and boundary artifacts in Chinese style migration of images, an improved Variational Autoencoder (VAE) method for dress style migration is proposed. Firstly, the Yolo v3 model is used to quickly identify the dress localization of the input image, then, the classical semantic segmentation algorithm (FCN) is used to finely delineate the desired dress style migration region twice, and finally, the trained VAE model is used to generate the migrated Chinese style image. The results show that, compared with the traditional style migration model, the improved VAE style migration model can obtain finer synthetic images for dress style migration and can adapt to different Chinese traditional styles to meet the application requirements of dress style migration scenarios. We evaluated several deep learning-based models and achieved a BLEU value of 0.6 on average. The transformer-based model outperformed the other models, achieving a BLEU value of up to 0.72. [ABSTRACT FROM AUTHOR]

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect. [ABSTRACT FROM AUTHOR]

In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, remote sensing image data plays an active role in the dynamic monitoring of global resource changes and land cover utilization. Remote sensing image land cover classification is an important application direction of remote sensing data; how to further improve the accuracy of remote sensing image land cover classification is very important for the effective application of remote sensing data. The traditional remote sensing image land cover classification is mainly to classify remote sensing data according to the spectral data of ground objects. However, due to the complex environment of remote sensing images and the dynamic changes of the environment, traditional classification methods based only on pixel spectral data are often unable to achieve. A satisfactory classification result is achieved. In addition, some researchers have also proposed to combine pixel neighborhood texture information to supplement spectral feature data. Although the traditional classification method based on spectral features solves the problem of time-consuming visual interpretation, to a certain extent, due to the limited semantic expression ability and poor generalization ability of the design features, the classification accuracy is still not very satisfactory. This paper mainly studies the classification method of land cover remote sensing image based on pattern recognition. This paper is based on the experimental results of remote sensing data in Nanjing Yuhuatai District in 2018 and 2019. The ground resolution of the data is 2.5 meters. Data is projected, corrected, and equalized. Half of the images covering 43.75 square kilometers are used as training samples, and the remaining 50 square kilometers are used for detection. In the classification results of this IndianPines data, OA only increased by nearly 10% to 86.2%, AA increased by 13%, r was 82.77%, and Kappa coefficient was 0.84. In the classification results of Salinas data, both OA and AA increase by about 5%, and the optimization effect is not obvious. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, the use and sharing of data have brought great opportunities and challenges to mankind. On the one hand, the development of data sharing and analysis technology has promoted the improvement of economic and social benefits. On the other hand, protecting private information has become an urgent issue in the Internet era. In addition, the amount and type of information data are also increasing. At present, most algorithms can only encrypt a single type of small-scale data, which cannot meet the current data environment. Therefore, it is very necessary to study the privacy protection algorithm of multimodal data fusion. To improve the security of privacy protection algorithm, combined with the idea of multimode, this paper combines the improved traditional spatial steganography algorithm LSB matching method and the improved traditional transform domain steganography algorithm DCT with AES encryption algorithm after modifying the S-box and then combines it with image stitching technology, so as to realize a safe and reliable privacy protection algorithm of multimode information fusion. The algorithm completes the hidden communication of private information, which not only ensures that the receiver can accurately recover private information in the process of information transmission but also greatly improves the security of private information transmission. [ABSTRACT FROM AUTHOR]

Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain. [ABSTRACT FROM AUTHOR]

A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets. [ABSTRACT FROM AUTHOR]

An automatic intelligent coloring model of animation sketch based on enhanced deep learning is proposed. In the proposed model, generative adversarial networks (GANS) are adopted. The U-net network based on the Swish function residual enhancement is used in the generative model, and the ResNet network is used in the discriminant model. The U-net embedded with the Swish Gate module is adopted to transmit feature map information. The perceptual network on the discriminator is used to perceive the perceptual features of the generated image and the actual image and calculate the perceptual loss. Experiment results show that perceptual loss can better capture the difference between black-and-white images and color images, so as to better train the network end-to-end. After comparative analysis, it can be concluded that compared with the existing methods, the proposed model has greater advantages in processing animation sketches. The color images it generates have higher visual quality and richer color diversity and matching. [ABSTRACT FROM AUTHOR]

This study was to explore the application value of magnetic resonance imaging (MRI) images obtained by low-rank matrix recovery algorithm (LRMR algorithm) in evaluating the curative effect of rectal cancer patients receiving the neoadjuvant chemo-radiotherapy (nCRT). In this study, an image denoising model was designed based on the LRMR algorithm, the original low-rank data matrix was recovered from the error, and the low-rank matrix was restored by solving the optimal kernel norm, so as to effectively separate the image data information and the interference noise. In addition, the model was applied to 60 patients with rectal cancer who received nCRT to extract the texture parameters and lesion-related data from the MRI images. The results showed that the MRI images optimized by LRMR algorithm were clearer than the original images, contained less excess noise, and had improved imaging accuracy and image quality. The results of typical cases suggested that the front of the rectal wall membrane of a patient in the T-downstage group was not smooth before treatment, the internal angiography was blurred, and the wall membrane was thickened, but the wall membrane became thinner after treatment, the highest position was reduced from 1.46 cm to 0.38 cm, the average value of the apparent diffusion coefficient (ADC) increased from 0.732 × 10−3 mm2/s to 1.196 × 10−3 mm2/s, and the lesion tissue was thicker. It was found that the height, length, and ADC of the lesion after the nCRT showed statistically great difference in contrast to the values before the treatment P < 0.05 . Such results indicated that the nCRT showed obvious effects in the clinical treatment of rectal cancer. In short, the LRMR algorithm could remove the interference noise in the MRI image, and from the information about rectal cancer tumor lesions extracted from that, the height value and length value of tumor lesions in patients given neoadjuvant chemo-radiotherapy were reduced compared with those before treatment, and the apparent diffusion coefficient value was increased, indicating that neoadjuvant chemo-radiotherapy has a significant effect in the clinical treatment of rectal cancer. [ABSTRACT FROM AUTHOR]

Texture fusion is the process of applying the style of one style image to another content image. It is an artistic creation and image editing technology. In recent years, the rapid development of deep learning has injected new power into the field of computer vision, and a large number of image style transfer algorithms based on deep learning have been proposed. At the same time, some current character conversion algorithms based on unsupervised learning also face the loss of the content and structure of the generated characters, and at the same time, they have not learned a good face deformation effect, resulting in poor image generation effects. This paper studies the relevant research background and research significance of image style transfer methods and summarizes them in the time sequence of their development; summarizes the style transfer algorithms based on deep learning, and analyzes the advantages and disadvantages of each type of algorithm. Based on the fast style transfer algorithm, this method adds a saliency detection network and designs a saliency loss function. In the training process, the difference between the saliency map of the generated image and the content image is additionally calculated, and the saliency loss is used as a part of the total loss for iterative training. Experiments show that the stylized image generated by this algorithm can better retain the salient area of the content image and has a good visual effect. Compared with the original network, the amount of parameters of the attention mechanism is very small, and there is almost no additional burden. [ABSTRACT FROM AUTHOR]

This work was aimed to establish a feature model for glioma grading and early metastasis and recurrence risk prediction based on contrast-enhanced magnetic resonance imaging (MRI). A total of 145 patients diagnosed with glioma by pathological examination were selected as the research subjects (training cohort: nasty 80; validation cohort: nasty 65). The imaging parameters T1-weighted (CET1WI), axial T2-weighted (T2WI), and apparent diffusion coefficient (ADC) were selected for the extraction of size and shape, intensity, histogram, and texture features. Image dimensionality reduction, feature selection, and model building were performed using the LASSO regression method. Using imaging features as potential predictors and evaluation indicators, the accuracy, sensitivity, and specificity of all prediction models and the area under the curve (AUC) of the receiver operating characteristic curve were calculated. Moreover, a predictive model for glioma grading and early metastasis risk was constructed. The results showed that under a single imaging parameter (T1-CE, DDC, T2WI-FLAIR, ADCslow, Alpha, ADC, CBF, and ADCfast), the diagnostic accuracy, sensitivity, specificity, AUC, and 95% confidence interval (CI) of low-grade gliomas (LGG), high-grade gliomas (HGG), and recurrent and nonrecurrent gliomas were significantly different (P < 0.05). The texture features, histogram features, and mean AUC of distinguishing low-grade and high-grade gliomas were 0.958, 0.945, and 0.954, respectively. The texture features, histogram features, and mean AUC for distinguishing recurrent and nonrecurrent gliomas were 0.949, 0.876, and 0.900, respectively. In short, the use of enhanced MRI imaging features can realize the prediction of early grading and recurrence of glioma and is of great significance for the early classification of benign and malignant characteristics of tumors. [ABSTRACT FROM AUTHOR]

Current music recommendation systems can explore the general relationship between the users and songs to recommend music to the users; however, they cannot distinguish the different preferences of different users for the same song. For example, a user may like a song because of the singer, while another user will like it not for the singer but just because of the composition of the song or its melody. A recommender system that knows this difference would be more effective in recommending music to the users. To this end, this paper proposes a music recommendation model based on multilayer attention representation, which learns song representations from multidimensions using user-attribute information and song content information, and mines the preference relationship between users and songs. In order to distinguish the differences in user preferences for multidomain features of songs, a feature-dependent attention network is designed; in order to distinguish the differences in user preferences for different historical behaviors and to explore the temporal dependence of user behaviors, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users' preferences for candidate songs and is used to generate recommendations. The experimental results on 30Music and MIGU datasets show that the proposed model achieves significant improvement in recall and MRR compared with the current recommendation models. [ABSTRACT FROM AUTHOR]

In the cross-media image reproduction technology, the accurate transfer and reproduction of colour between different media are an important issue in the reproduction process, and the colour mapping technology is the key technology to effectively maintain the image details and improve the level of colour reproduction. Wooden structure in the image colour and colour piece is different, the image of each colour of visual perception is not independent, and every colour in the image pixels is affected by the surrounding pixels, but in the process of image map, without thinking of the pixel space, adjacent pixels of mutual influence in particular, do not let a person particularly be satisfied with the resulting map figure. In the process of image processing by traditional colour mapping algorithm, the colour distortion caused by colour component is ignored and the block diagram of colour mapping system is constructed. With the continuous development of mapping recognition algorithms, the maximum and minimum brightness values in the image are mapped to the maximum and minimum brightness values of the display device by linear mapping algorithm according to the flow of the established recognition algorithm. By establishing the colour adjustment method of the colour mapping image, the processing effect of the mapping algorithm is analysed. The results show that the brightness deviation of the image is reduced and the colour resolution is improved by the colour brightness compensation. [ABSTRACT FROM AUTHOR]