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In this paper, we first studied the action recognition method based on image texture features, established a human body model, and realized the extraction of skeletal data and joint calibration. Based on the skeleton information, the aerobic movements are recognized, the difference between the aerobics movement sequence and the standard movement sequence is calculated, and the movements are scored. Then, the database for aerobic movements was established, and an AI-assistant aerobics teaching model was developed. Finally, the effect of the teaching method was analyzed through indicators of recognition effect, skill impact, and students’ learning interests. The results show that the accuracy of the system’s action recognition reaches 95%, and the overall response time is between 5.2-6.4s, with high real-time performance. And it has a significant effect on students’ movement participation, active interest, and independent learning behavior, with p-values of 0.013, 0.041, and 0.036, respectively, p

Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.

In this paper, the importance of modern packaging design and pattern design and the application of pattern design in modern packaging design are the first two aspects of achieving the development of information integration of packaging design and pattern design, and the visual design based on Markov random field is proposed for the image retrieval and classification problems in traditional model packaging design and pattern design. Wavelet transforms, multi-scale analysis of image texture features and region merging conditions in visual design using GBIS algorithm, and image retrieval in pattern design are examined and analyzed. The results show that the model trained with both loss functions has the best retrieval top-k accuracy on both datasets, reaching 54.3% on the DARN dataset and 3.9% accuracy on Deep Fashion. This paper demonstrates the model’s effectiveness for modifying and retrieving pattern design image attributes.

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, existing center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiple threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiple threshold encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into a 3-bit MTCP. Furthermore, this paper proposes a completed multiple threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiple threshold encoding pattern achieves superior texture classification performance.

Rejoining oracle bone fragments plays an import role in studying the history and culture of the Shang dynasty by its characters. However, current computer vision technology has a low accuracy in judging whether the texture of oracle bone fragment image pairs can be put back together. When rejoining fragment images, the coordinate sequence and texture features of edge pixels from original and target fragment images form a continuous symmetrical structure, so we put forward an internal similarity network (ISN) to rejoin the fragment image automatically. Firstly, an edge equidistant matching (EEM) algorithm was given to search similar coordinate sequences of edge segment pairs on the fragment image contours and to locally match the edge coordinate sequence of an oracle bone fragment image. Then, a target mask-based method was designed in order to put two images into a whole and to cut a local region image by the local matching edge. Next, we calculated a convolution feature gradient map (CFGM) of the local region image texture, and an internal similarity pooling (ISP) layer was proposed to compute the internal similarity of the convolution feature gradient map. Finally, ISN was contributed in order to evaluate a similarity score of a local region image texture and to determine whether two fragment images are a coherent whole. The experiments show that the correct judgement probability of ISN is higher than 90% in actual rejoining work and that our method searched 37 pairs of correctly rejoined oracle bone fragment images that have not been discovered by archaeologists.

In this paper, we propose an adaptive gamma transform that adjusts the local values of bright and dark parts to enhance the effect of low-illumination images, thereby improving the light component. We then apply diff texture enhancement to enhance the contrast of images processed by the Retinex algorithm, thereby optimizing the perception of overexposed and underexposed imagery. Analyze the effect of image brightness enhancement based on a nonlinear transformation combined with the LOL dataset. Use PSNR and SSIM image quality evaluation criteria to analyze the visual effect of improving low-illumination images based on Retinex theory. Create a dataset of power marketing field operation inspection images and examine the effects of overexposure and underexposure image processing on four types of images: high-voltage towers, transmission lines, high-voltage fixtures, and high-voltage wireframes, using the low-light image texture fusion algorithm based on Retinex theory. Overall, this paper’s algorithm and the three DeblurGAN and DMCNN models achieve the effect of deblurring overexposed and underexposed power marketing field operation inspection images. From the local details, the model in this paper has a better effect on the de-exposure of the image, which can provide effective help for the electric power staff to understand the situation of the electric power marketing operation site and has strong practicality.

With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.

In this paper, the color image is converted to a grayscale image in the image recognition preprocessing stage to accelerate the image recognition processing, and then the image contrast is enhanced by grayscale stretching to compute the grayscale layer covariance matrix and image texture features. Multi-step Markov clustering method is proposed to optimize the GCN, and the instance normalization layer and batch normalization layer are added to strengthen the source domain representation ability of the GCN to form a cross-domain image recognition algorithm based on a pairwise generalization network. Elaborate the visual design path of image information by artificial intelligence image recognition and image processing technology, introduce image recognition technology into the field of visual design, establish a visual design partition model, and completely extract the local feature information of computer image graphics. Use the dataset to evaluate the performance of pairwise generalization networks, and conduct simulation experiments to analyze the visual expression effects of visual design. The PGN-RM method, with the addition of maximum mean distance, instance normalization, and batch normalization, is able to achieve a performance average of 91.843. The peak signal-to-noise ratio of the actual effect image of the visual design of the product packaging is maintained in the range of [95.0312, 97.0032], which is an excellent visual design effect. Visual design that utilizes artificial intelligence graphic recognition technology can express design ideas more deeply and enhance the visual design’s attractiveness.

This paper establishes different detail-level terrain grids for different terrain data blocks and generates multi-resolution terrain based on the tree-structured LOD model as a way of layering large-scale terrain data. Then, the virtual environment’s visual and image texture are rendered to visualize the virtual spatial environment. Then, the overall architecture of the interaction platform was designed, and the virtual space design visualization and interaction effects were studied based on spatial distance perception, presence experience perception, and error analysis. The results show that there is a significant difference between the mean value of people’s perceived satisfaction in the interaction space and non-interaction space M=163.138, t=1.376, p=0.005

Visual communication design and printmaking art are two independent art disciplines, but they have many similarities in graphic design, color matching and other aspects. This paper demonstrates the extraction of printmaking features using the improved LBP algorithm. The method of color histogram and color space is used to complete the color extraction of the image, and the texture features of the image are described by combining signal processing and statistical analysis. The LBP algorithm is used to locate the center pixel of the image to form a binary bit string, and the pattern value is obtained by multiplying each binary bit by the corresponding weight and summing up. The efficiency of image texture acquisition is enhanced by adding neighborhood gradient multiscale to the LBP algorithm. To obtain printmaking art elements and propose a direction for integration with visual communication design, image feature extraction is applied to printmaking art element extraction. The performance of texture classification for images has been significantly improved by the improved LBP algorithm, as shown in the results. On the Outextc10 texture dataset, DLBP5 achieves the highest classification accuracy of 92.19% by extracting colors, textures and shapes in printmaking and applying the features with elements of printmaking to visual communication design. Under the direction of integrating printmaking art style elements with visual communication design, users selected {unique, packaging design}, {vivid, branding design}, and {sharp, advertisement design} with higher satisfaction gradients, with ratings of 4.62, 4.17, and 4.36, respectively, which are all greater than 4.

Noise in an image can affect one’s extraction of image information, therefore, image denoising is an important image pre-processing process. Many of the existing models have a large number of estimated parameters, which increases the time complexity of the model solution and the achieved denoising effect is less than ideal. As a result, in this paper, an improved image-denoising algorithm is proposed based on the TV model, which effectively solves the above problems. The L1 regularization term can make the solution generated by the model sparser, thus facilitating the recovery of high-quality images. Reducing the number of estimated parameters, while using the inverse gradient to estimate the regularization parameters, enables the parameters to achieve global adaption and improves the denoising effect of the model in combination with the TV regularization term. The split Bregman iteration method is used to decouple the model into several related subproblems, and the solutions of the coordinated subproblems are derived as optimal solutions. It is also shown that the solution of the model converges to a Karush–Kuhn–Tucker point. Experimental results show that the algorithm in this paper is more effective in both preserving image texture structure and suppressing image noise.

Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability. Moreover, the constructed deblurring model is difficult to solve, and the operation is comparatively complicated. Meanwhile, deep learning has become a hotspot in various fields in recent years. End-to-end convolutional neural networks (CNNs) can learn the pixel mapping relationships between degraded images and clear images. In addition, they can also obtain the result of effectively eliminating spatial variable blurring. However, conventional CNNs have some disadvantages in generalization ability and details of the restored image. Therefore, this paper presents an iterative dual CNN called IDC for image deblurring, where the task of image deblurring is divided into two sub-networks: deblurring and detail restoration. The deblurring sub-network adopts a U-Net structure to learn the semantical and structural features of the image, and the detail restoration sub-network utilizes a shallow and wide structure without downsampling, where only the image texture features are extracted. Finally, to obtain the deblurred image, this paper presents a multiscale iterative strategy that effectively improves the robustness and precision of the model. The experimental results showed that the proposed method has an excellent effect of deblurring on a real blurred image dataset and is suitable for various real application scenes.

Following the traditional total variational denoising model in removing medical image noise with blurred image texture details, among other problems, an adaptive medical image fractional-order total variational denoising model with an improved sparrow search algorithm is proposed in this study. This algorithm combines the characteristics of fractional-order differential operators and total variational models. The model preserves the weak texture region of the image improvement based on the unique amplitude-frequency characteristics of the fractional-order differential operator. The order of the fractional-order differential operator is adaptively determined by the improved sparrow search algorithm using both the sine search strategy and the diversity variation processing strategy, which can greatly improve the denoising ability of the fractional-order differential operator. The experimental results reveal that the model not only achieves the adaptivity of fractional-order total variable differential order, but also can effectively remove noise, preserve the texture structure of the image to the maximum extent, and improve the peak signal-to-noise ratio of the image; it also displays favorable prospects for applications in medical image denoising.