Image Texture Research Articles

Multi-Modal Fusion Based on Depth Adaptive Mechanism for 3D Object Detection

Lidars and cameras are critical sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, accurate and robust fusion methods are still under exploration due to non-homogenous representations. In this paper, we find that the complementary roles of point clouds and images vary with depth. An important reason is that the point cloud appearance changes significantly with increasing distance from the Lidar, while the image's edge, color, and texture information are not sensitive to depth. To address this, we propose a fusion module based on the Depth Attention Mechanism (DAM), which mainly consists of two operations: gated feature generation and point cloud division. The former adaptively learns the importance of bimodal features without additional annotations, while the latter divides point clouds to achieve differential fusion of multi-modal features at different depths. This fusion module can enhance the representation ability of original features for different point sets and provide more comprehensive features by using the dual splicing strategy of concatenation and index connection. Additionally, considering point density as a feature and its negative correlation with depth, we build an Adaptive Threshold Generation Network (ATGN) to generate the depth threshold by extracting density information, which can divide point clouds more reasonably. Experiments on the KITTI dataset demonstrate the effectiveness and competitiveness of our proposed models.

ENHANCEMENT OF MEDICAL MRI IMAGES BASED ON FRACTAL OPERATORS

This article presents the research of texture enhancement algorithms on medical images. Medical MRI brain scans contain large areas with low level grey colors that carry useful information for doctors. Texture improvement allow to highlight large grey areas on images for future detailed recognition. Based on the study of existing texture enhancement methods, it was determined that fractal operators are effective for processing medical images. The mathematical framework of fractal operators is presented based on the approximation equation of the Grünwald-Letnikov fractional derivatives. The creation of fractal differential masks and the algorithm of masks usage for image enhancement are described based on this equation. The approximation error of the Grunwald-Letnikov derivative is calculated in comparison with the analytical value of the Grunwald-Letnikov derivative. The algorithm based on the fractal derivative shows improvements in image parameters such as contrast, correlation, energy, and homogeneity compared to the original image parameters. A comparison of the results of the algorithm based on the fractal differential with other algorithms for improving the texture of images is also given. It is concluded that the fractal differential algorithm is well-suited for MRI image enhancement tasks, unlike other algorithms, both in visual comparisons and numerical metrics, and thus can be applied to solve real-world problems.

Enhancing the Visual Effectiveness of Overexposed and Underexposed Images in Power Marketing Field Operations Using Gray Scale Logarithmic Transformation and Histogram Equalization

Abstract 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.

Low Complexity Decision Algorithm for CU Partition Based on Image Texture Information and LGBM

Low Complexity Decision Algorithm for CU Partition Based on Image Texture Information and LGBM

Information communication and aesthetic value of data visualization in the field of art and design

Abstract Data visualization has become a popular discipline in today’s society, and understanding this trend has become a hot topic in the field of design. This paper introduces the visual perception system of the human eye, combining graphic features and dynamic features to reveal the visualization mechanism of information dynamic conveying in the field of art design. Then, the quantitative model of visual communication in color and graphic texture in art design, respectively, and the art design image containing rich plants are taken as an example to realize the visual analysis of information communication in the field of art design. There is a significant negative correlation (p > 0.05) between the purity of the foreground image, the foreground image color gamut area, and the proportion of foreground image color matching. This means that the higher the purity of the foreground image, the smaller the color matching area, leading to more coordinated color matching and a higher aesthetic degree. Simultaneously, the alteration in the gray value of the image texture typically exhibits characteristics such as high frequency, small amplitude, and randomness. The use of visualization technology in art design, reasonable collocation of colors, and graphic aesthetics fully demonstrate its aesthetic value.

Traditional art design expression based on embedded system development.

This article describes constructing an embedded system for a painting art and style presentation platform, achieving the automatic integration of digital painting art with traditional art design. The frontend components are designed using the Bootstrap framework, with Django as the web development framework and TensorFlow architecture integrated into the code. Furthermore, the Inception module and residual connections are introduced to optimize the visual geometry group (VGG) network for recognizing and analyzing image texture features. Compared to other models, experimental results indicate that the proposed model demonstrates a 2.6% increase in image style classification accuracy, reaching 87.34% and 95.33% in architectural and landscape image classification, respectively. The system's operational outcomes reveal that the proposed platform alleviates the burden on the logical function modules of the system, enhances scalability, and promotes the automated fusion of digital painting art with traditional art design expression.

Optical Sensor-Based Image Texture Characterization for Image Simulation Applications in College Gymnastics Instruction

Abstract Gymnastics learning is characterized by imitation and reference, and a teaching model combining optical sensors and image simulation systems may have promising results in college gymnastics courses. Based on the introduction of the meaning of college gymnastics teaching, the article sorted out the value performance of the image teaching mode and also established an optical three-dimensional imaging system based on optical sensors for gymnastics movement to obtain gymnastic images. In the optical 3D imaging system, the acquisition error of gymnastics images is reduced by using the finite distance image measurement method, and the quality of gymnastics images is enhanced by adaptive bilateral filtering and adaptive threshold selection of the Canny operator by the OTSU method. The enhanced gymnastics images are then inputted into a panoramic graphic imaging simulation system, and a gymnastics image simulation teaching model is established for colleges and universities. The effectiveness of the gymnastics image simulation teaching model is analyzed through a teaching comparison test conducted during simulation verification for optical imaging. In the optical imaging system, when the measurement distance is about 150m, the measurement error range is between [15″and 30″], and the MSE value of the gymnastic enhanced image obtained by the improved Canny algorithm decreases by 46.97% compared with the MSE value before the improvement. The students’ handstand score against the wall improved by 22.63 points compared to before the experiment, and more than 90% of the students were satisfied with the gymnastics image simulation teaching method. The teaching mode is constructed with the optical sensor to obtain the image characteristics of gymnastics movement, and the simulation system is innovative, which effectively promotes students’ interest in learning gymnastics courses and enhances their gymnastics training level.

Mutual Dual-Task Generator With Adaptive Attention Fusion for Image Inpainting

Image segmentation can reveal the semantic structure information in an image, which is helpful guidance information for image inpainting. Notably, it can help mitigate the artifacts on the boundaries of different semantic regions during the inpainting process. Existing semantic guidance-based image inpainting provides one-way guidance from the semantic segmentation task to the image inpainting task. There is no feedback from the inpainting results to adjust the guidance process, which causes inferior performance. To tackle this issue, this work proposes mutual dual-task generators to establish the interaction between image segmentation and image inpainting tasks. Thus, semantic segmentation guides image inpainting and also receives feedback from image inpainting. These two processes interact with each other and progressively improve the inpainting quality. The mutual dual-task generator consists of a shared encoder and mutual decoders with the bidirectional Cross-domain Feature DeNormalization (CFDN) module inside, which hierarchically models the Segmentation-guided image Texture (ST) generation and Texture-guided semantic Segmentation (TS) generation. At the end of mutual decoders, an Adaptive Attention Fusion (AAF) module is proposed to augment the texture and semantic class affinity between pixels, further refining the inpainted results. Experimental results demonstrate that the proposed mutual dual-task generator pipeline achieves superior inpainting performances over the state of the arts on three public datasets.

Image Quality and Lesion Detection of Multiplanar Reconstruction Images Using Deep Learning: Comparison with Hybrid Iterative Reconstruction.

We assessed and compared the image quality of normal and pathologic structures as well as the image noise in chest computed tomography images using "adaptive statistical iterative reconstruction-V" (ASiR-V) or deep learning reconstruction "TrueFidelity". Forty consecutive patients with suspected lung disease were evaluated. The 1.25-mm axial images and 2.0-mm coronal multiplanar images were reconstructed under the following three conditions: (i) ASiR-V, lung kernel with 60% of ASiR-V; (ii) TF-M, standard kernel, image filter (Lung) with TrueFidelity at medium strength; and (iii) TF-H, standard kernel, image filter (Lung) with TrueFidelity at high strength. Two radiologists (readers) independently evaluated the image quality of anatomic structures using a scale ranging from 1 (best) to 5 (worst). In addition, readers ranked their image preference. Objective image noise was measured using a circular region of interest in the lung parenchyma. Subjective image quality scores, total scores for normal and abnormal structures, and lesion detection were compared using Wilcoxon's signed-rank test. Objective image quality was compared using Student's paired t-test and Wilcoxon's signed-rank test. The Bonferroni correction was applied to the P value, and significance was assumed only for values of P < 0.016. Both readers rated TF-M and TF-H images significantly better than ASiR-V images in terms of visualization of the centrilobular region in axial images. The preference score of TF-M and TF-H images for reader 1 were better than that of ASiR-V images, and the preference score of TF-H images for reader 2 were significantly better than that of ASiR-V and TF-M images. TF-M images showed significantly lower objective image noise than ASiR-V or TF-H images. TrueFidelity showed better image quality, especially in the centrilobular region, than ASiR-V in subjective and objective evaluations. In addition, the image texture preference for TrueFidelity was better than that for ASiR-V.

Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

Quality Assessment of Selective Laser Melting Components Using Quantitative Ultrasound Measurements and Image Texture Analysis

Research on Image Recognition and Visual Design Based on Artificial Intelligence

Abstract 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.

Fractal Few-Shot Learning.

Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while improving the accuracy is still a major challenge. To address this problem, this article considers the consistency between similar data from the fractal perspective, introduces a priori knowledge, and proposes a fractal embedding model by combining FSL with fractal dimension theory for the first time. We improve the original fractal dimension algorithm used to describe image texture roughness to suit a neural network. Moreover, in accordance with the improved algorithm, prior knowledge of the quantized image is integrated into the features to reduce the impact of the data distribution on the model. Experimental results obtained on multiple image benchmark datasets show that the performance of the proposed model exceeds or matches that of previous state-of-the-art models. In addition, the proposed model achieves the best performance in cross-domain scenarios, further illustrating its robustness.

Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

AbstractFresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods ( – 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions.

Gray Level Homogeneity Analysis: A Novel Approach

In this article, we propose a method that helps us to analyze the homogeneity of gray levels locally by calculating a coefficient for each pixel based on the nature of neighboring pixels. This principle of encoding pixels according to their adjacent neighbors is described the nature of the distribution of gray levels within the image and measures their degree of homogeneity locally. This allows us to detect the different regions of the image and their contours based on the coefficient of homogeneity of the gray levels. In addition, this allows us to exploit these homogeneity coefficients to restructure regions of the image, extract and enhance the image contours while reducing the noise present in the image. This homogeneity study principle has several functions in the study and analysis of image texture, as do other methods of homogeneity assessment, such as the local contrast descriptor (LCD) and the co-occurrence matrix.

Fast Thick Cloud Removal for Multi-Temporal Remote Sensing Imagery via Representation Coefficient Total Variation

Although thick cloud removal is a complex task, the past decades have witnessed the remarkable development of tensor-completion-based techniques. Nonetheless, they require substantial computational resources and may suffer from checkboard artifacts. This study presents a novel technique to address this challenging task using representation coefficient total variation (RCTV), which imposes a total variation regularizer on decomposed data. The proposed approach enhances cloud removal performance while effectively preserving the textures with high speed. The experimental results confirm the efficiency of our method in restoring image textures, demonstrating its superior performance compared to state-of-the-art techniques.

Measuring the Macrotexture of Pavement Surface Using an Image Processing Technique

The objective of this study was to propose an image processing-based index for measuring pavement macrotexture at the network level. This index enables macrotexture to be measured easily and inexpensively using images collected at traffic speed. The study involved collecting pavement surface images at a constant traffic speed on a test section specifically designed and constructed for this purpose, with three surface course mixes that are commonly used in India, namely, bituminous concrete, stone matrix asphalt, and gap-graded rubberized bituminous mix. Additionally, macrotexture data with regard to mean texture depth (MTD) from the sand patch test and mean profile depth (MPD) from laser sensor-based measurements were obtained at the locations where the images were captured. The surface macrotexture index (SMI), which was derived from wavelet transform-based image texture analysis, was compared with the MTD and MPD data. The results showed that the SMI is an accurate indicator of pavement surface macrotexture. In addition, the study showcased the application of an unsupervised machine learning algorithm to identify and replace outliers in the SMI data that resulted from isolated spots with dirt, pavement markings, and wet surfaces. The research also established relationships between the proposed SMI and MTD/MPD. These relationships are reliable and can be used to predict the commonly used pavement surface construction quality measure MTD and the network-level skid resistance indicator MPD.

Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone.

Accurate differentiation of benign and malignant cervical lymph nodes is important for prognosis and treatment planning in patients with head and neck squamous cell carcinoma. We evaluated the diagnostic performance of magnetic resonance image (MRI) texture analysis and traditional 18F-deoxyglucose positron emission tomography (FDG-PET) features. This retrospective study included 21 patients with head and neck squamous cell carcinoma. We used texture analysis of MRI and FDG-PET features to evaluate 109 histologically confirmed cervical lymph nodes (41 metastatic, 68 benign). Predictive models were evaluated using area under the curve (AUC). Significant differences were observed between benign and malignant cervical lymph nodes for 36 of 41 texture features (p < 0.05). A combination of 22 MRI texture features discriminated benign and malignant nodal disease with AUC, sensitivity, and specificity of 0.952, 92.7%, and 86.7%, which was comparable to maximum short-axis diameter, lymph node morphology, and maximum standard uptake value (SUVmax). The addition of MRI texture features to traditional FDG-PET features differentiated these groups with the greatest AUC, sensitivity, and specificity (0.989, 97.5%, and 94.1%). The addition of the MRI texture feature to lymph node morphology improved nodal assessment specificity from 70.6% to 88.2% among FDG-PET indeterminate lymph nodes. Texture features are useful for differentiating benign and malignant cervical lymph nodes in patients with head and neck squamous cell carcinoma. Lymph node morphology and SUVmax remain accurate tools. Specificity is improved by the addition of MRI texture features among FDG-PET indeterminate lymph nodes. This approach is useful for differentiating benign and malignant cervical lymph nodes.

A Method of Printmaking Image Generation Based on Convolutional Neural Network

In this paper, a novel image reconstruction method for printmaking is proposed via the combination of nonlinear diffusion filtering and convolution neural network. Nonlinear diffusion filtering based on a partial differential equation abstracts the input image to extract color features and texture features of images by nonlinear structured tensors. The image color features and texture features are combined to obtain a feature vector, and the feature vector is fed into the model as the input data. Then, the pre-trained deep learning model VGG-19 is utilized as the backbone network for further feature representation, and the extracted feature maps of each layer of the VGG19 model can be visualized for image reconstruction. The quality of the reconstructed images of prints is improved with the powerful feature extraction and combination ability of neural networks, which has the advantages of fast reconstruction speed and high quality of reconstructed images. Finally, the simulation experiments can demonstrate that the proposed reconstruction approach can achieve relatively ideal reconstruction performance in scenes of printmaking.

The Quality Assessment of Sour Cherries Dried Using an Innovative Simultaneous Osmotic–Microwave–Vacuum Approach Based on Image Textures, Color Parameters, and Sensory Attributes

Sour cherries are a perishable raw material, and their preservation is needed to extend their availability to consumers. Improving drying techniques is desirable to ensure the highest quality of products. This study aimed to determine image textures from color channels R, G, B, L, a, b, X, Y, and Z; color parameters L*, a*, and b*; the color difference (ΔE) of raw materials and dried fruit; and the sensory attributes of dried sour cherry products prepared using an innovative approach. Three sour cherry cultivars, ‘Nefris’, ‘Debreceni Botermo’, and ‘Łutówka’, were used in the experiment. Sour cherries were subjected to freezing and pit removal before drying. The simultaneous osmotic–microwave–vacuum drying was carried out in one process lasting an hour and combining osmotic dehydration using a 40 °Bx sucrose solution and microwave–vacuum drying at microwave powers of 100 W for 900 s, 300 W for 900 s, 250 W for 900 s, and 0 W for stabilization for 900 s and a pressure of 30 ± 2 hPa. After drying, the quality assessment of products was performed using non-destructive image analysis and color measurements, as well as sensory analysis, including non-destructively and destructively assessed attributes. The highest changes in textures occurred for the GHMean (histogram’s mean for color channel G) (from 30.69 to 22.64) and LHMean (histogram’s mean for color channel L) (from 66.93 to 59.07) of images of the cultivar ‘Łutówka’, and the smallest changes were found for the cultivar ‘Nefris’. Drying had a statistically significant effect on the color parameters of the ‘Debreceni Botermo’ and ‘Łutówka’ sour cherries. The value of ΔE was the highest (10.44) for ‘Debreceni Botermo’ and the smallest (1.98) for ‘Nefris’. All cultivars of dried sour cherries had very high values of overall quality, reaching 8.9 for ‘Nefris’ and ‘Debreceni Botermo’ and 8.8 for ‘Łutówka’. The ‘Nefris’ sour cherry was characterized by the highest value of flavor of 9.0. All dried samples were attractive in terms of their external appearance. The sensory parameters related to taste, texture, and crunchiness were also satisfactory. Innovative simultaneous osmotic–microwave–vacuum drying allowed for the obtainment of dried sour cherries with a high quality, including acceptable sensory attributes.

The application of printmaking art elements in visual communication design

Abstract 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.