Complex Texture Research Articles

Inelastic rotations and pseudoturbulent plastic avalanches in crystals.

Plastic deformations in crystals produce microstructures with randomly oriented patches of unstressed lattice forming complex textures. We use a mesoscopic Landau-type tensorial model of crystal plasticity to show that in such textures rotations can originate from crystallographically exact microslips which self organize in the form of laminates of a pseudotwin type. The formation of such laminates can be viewed as an effective internal "wrinkling" of the crystal lattice. While such "wrinkling" disguises itself as an elastically neutral rotation, behind it is inherently dissipative, dislocation-mediated process. Our numerical experiments reveal pseudoturbulent effective rotations with power-law distributed spatial correlations which suggests that the process of dislocational self-organization is inherently unstable and points toward the necessity of a probabilistic description of crystal plasticity.

Adaptive HEVC video steganography based on distortion compensation optimization

With the development of digital video communication, the steganography based on H.265/HEVC has attracted more and more attention. Because of higher security and better compatibility, the traditional video steganography often used quantized discrete cosine transform (QDCT) or quantized discrete sine transform (QDST) coefficients to embed secret messages. However, the QDCT/QDST-based steganographic methods will cause distortion drift seriously. Although the distortion compensation optimization method can eliminate the intra-frame distortion drift, it ignored the influence of inter-frame distortion. This limitation prompts authors to propose an adaptive HEVC steganography, which eliminates the intra-frame distortion drift while reducing inter-frame distortion. First, the distortion of intra-block and inter-block is analyzed with the prediction and transform process. Then, the corresponding embedding influence is defined, which includes intra-block distortion, inter-frame distortion accumulation, the block texture complexity and the cost of coefficient groups. Finally, the proposed adaptive cost function is designed based on the above definition and the syndrome trellis codes (STC) is used for embedding. The experimental results show that the proposed method has better performance compared with the state-of-the-art coefficient-based steganography in visual quality, bit rate increment and anti-steganalysis.

Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

Multiclass Gastrointestinal Diseases Classification Based on Hybrid Features and Duo Feature Selection

Gastrointestinal Tract (GIT) infections are quite common nowadays. If these abnormalities are left untreated at early stages, they may develop into stomach cancers. Wireless Capsule Endoscopy (WCE) is a method that enables medical professionals to view the internal parts of the GIT and take pictures using a pill camera. Manual detection of abnormalities from the taken images is time-consuming and may lead to misdiagnosis. Several Computer-based methods were developed in this domain, but improving prediction accuracy is still challenging due to the complex textures, colours, irregularities of tissues and quality of images. To address this issue, a novel technique has been introduced in this research based on color, texture, statistical, shape and deep pretrained Densenet features from contrast-enhanced GI images. The extracted features are fused to form a powerful features subset. From the fused features, the minimal-optimal feature subset is selected using the two-stage ReliefF-minimum Redundancy Maximum Relevance (R-mRMR) method and fed to One Against All Support Vector Machine (OAA-SVM) for classification. The proposed work is validated using 8000 images with eight classes of KVASIR V2 and attained the maximum classification accuracy of 99.2% and precision of 99.1%.

CACFNet: Fabric defect detection via context-aware attention cascaded feedback network

Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably.

Advancing textural heterogeneity: Effect of manipulating multi-component model foods on the perception of textural complexity

The aim of this study was to identify the individual and interacting effects of varying the mechanical properties of two inserts (к-carrageenan beads; 1, 2 and 4% w/w and/or agar-based disks; 0.3, 1.2 and 3% w/w) in pectin-based gels on the perception of textural complexity.A full factorial design was utilised, 16 samples were characterised with sensory and instrumental tests. Rate-All-That-Apply (RATA) was performed by 50 untrained participants. RATA selection frequency provided different information to attribute intensity regarding the detection of low yield stress inserts.In the two-component samples, the perception of textural complexity (n = 89) increased with insert yield stress for both к-carrageenan beads and agar disks. However, with the addition of medium and high yield stress к-carrageenan beads to three-component samples, the increases in perceived textural complexity caused by increased agar yield stress were eliminated.The definition of textural complexity, the number and intensity of texture sensations, as well as their interactions and contrasts, was in line with the results, and the hypothesis that not only mechanical properties but also the interaction of components play a key role in the perception of textural complexity.

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework

Background and Objective: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions. Methods: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification. Specifically, we first construct a spatial context aggregation module to generate multi-scale context representations from the original BUS images. Subsequently, multi-scale representations are efficiently fused in feature fusion block that is armed with special fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in the feature fusion block to learn and combine multi-order representations. Finally, these multi-scale gradational-order representations are utilized to perform prediction for the malignant potential of breast lesions. Results: The proposed model was evaluated on three open datasets by using 5-fold cross-validation. The experimental results (Accuracy: 85.32%, Sensitivity: 85.24%, Specificity: 88.57%, AUC: 90.63% on dataset A; Accuracy: 76.48%, Sensitivity: 72.45%, Specificity: 80.42%, AUC: 78.98% on dataset B) demonstrate that the proposed method achieves the promising performance when compared with other deep learning-based methods in BUS classification task. Conclusions: The proposed method has demonstrated a promising potential to predict malignant potential of breast lesion using ultrasound image in an end-to-end manner.

A Novel Image Encryption Algorithm Based on Multiple Random DNA Coding and Annealing

Improved encryption devices place higher demands on the randomness and security of encrypted images. Existing image encryption optimization methods based on single- or multi-objectives concentrate on selecting keys and parameters, resulting in relatively fixed parameters and keys that are susceptible to leakage and cracking. Despite the possibility of increasing security, the DNA coding encryption method does not fully take into account the large capacity of image data and the difference between pixels, resulting in a limited level of randomness. To overcome the problems above, this paper proposes a method for generating complex texture features in images using random variation of pixels. With an annealing algorithm that can find an optimal solution in a large search space, the image is optimally optimized in terms of information entropy, pixel correlation, and value of x2. Each iteration involves selecting one of 25632 combinations of DNA coding and operation. In comparison with current encryption algorithms based on optimization algorithms and DNA coding, this method is more secure and unbreakable.

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification.

Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

Ludovico Effect of Intermediality in A Clockwork Orange


 
 
 
 During the intensive critical debates around the adaptation of Burgess’s novel, which mainly raised the issues of fidelity and obscenity, Stanley Kubrick repeatedly claimed “[i]t’s all in the plot.” By saying this, he was not only referring to a narrative fidelity to the plot, but also to the inherent intermediality of Burgess’s text. Indeed, beyond the obvious tense duality between the orality represented by the nadsat slang and the literary, written text, the novel presents a complex texture of sensorial—visual, auditory, tactile, and even olfactory—cues that acquire culturally determined meanings in the novel and as such reflect on each other metaphorically. In order to avoid falling into the trap of a comparative criticism claiming the impossibility to adapt visual (or “sensorial”) literature to film, I will regard Kubrick’s adaptation as a discursive practice that adapts, beyond the plot, Burgess’s view on the manipulative, conditioning effect of audio-visual media on society. I argue that by irony and excess, achieved with audio-visual stylisation, Kubrick also thematises the contemporary criticism of these media effects. Moreover, he is not only representing but also modelling the central issue of socio-psychological conditioning through audio-visual exposure.
 
 
 

Influence of nest box design on internal microclimate: Comparisons of plastic prototypes

AbstractHollow‐dependent fauna are declining worldwide, due primarily to the widespread clearing of hollow‐bearing trees. Artificial cavities such as timber and plywood boxes are commonly used to increase hollow availability, yet there is increasing evidence that they are poor facsimiles of natural cavities, characterized by lower insulative properties and a shorter field life. We evaluated whether plastic materials could create a nest box with a stable thermal profile that more closely resembles the complex shapes and textures of natural tree hollows while containing fewer mechanical joins that represent potential failure points when installed. We developed three sets of prototype nest boxes comprising various combinations of plastic density (10%, 25% and 50%), insulation (single vs. double wall with or without sawdust between them), nesting chamber (with or without timber inserts) and bedding (with or without decomposed heartwood) and compared their thermal performance in a temperature‐controlled laboratory to compare internal temperature and relative humidity. We found double‐walled plastic nest box with an internal timber‐lined chamber was best able to buffer ambient temperature fluctuations, consistently recording internal temperatures of 6+°C below maximum ambient temperature, maintaining high levels of relative humidity (76%–92%) when furnished with decomposed timber heartwood. This design also performed better during a simulated hot day; internal temperatures exhibiting twice the lag time of single‐walled designs, noting that plastic density had little influence on internal conditions. While the recruitment and protection of hollow‐bearing trees must be a priority, this work shows significant potential in improving the design and functionality of artificial hollows that are critical to the conservation of hollow‐dependent species.

FAEC‐GAN: An unsupervised face‐to‐anime translation based on edge enhancement and coordinate attention

AbstractAnimation is a widely loved artistic form with high abstraction and powerful expression. The task of image translation from face to anime involves complex geometric and texture transformations, and requires the generated images with clear lines. The existing unsupervised image translation frameworks are often ineffective for this task. According to the characteristics of animation image, we propose an animation translation method based on edge enhancement and coordinate attention, which is called FAEC‐GAN. We design a novel edge discrimination network to identify the edge features of images, so that the generated anime images can present clear and coherent lines. And the coordinate attention module is introduced in the encoder to adapt the model to the geometric changes in translation, so as to produce more realistic animation images. In addition, our method combines the focal frequency loss and pixel loss, which can pay attention to both the frequency domain information and pixel information of the generated image to improve the visual effect of the image. The experimental results demonstrate that FAEC‐GAN is superior to the state‐of‐the‐art methods in the task of face‐to‐animation image translation.

Fast CU Decision Algorithm Based on Texture Complexity and CNN for VVC

Versatile Video Coding (VVC) introduces many advanced video coding techniques. These advanced video coding techniques not only improve video compression efficiency and video quality, but also greatly increase coding time and computational complexity. Therefore, this paper uses a fast CU partitioning decision algorithm based on texture complexity and convolutional neural networks (CNN). First, we performed statistics and analysis of CU segmentation patterns for videos in the standard training set, then processed large blocks of CU based on texture complexity. To realize the purpose of fast partitioning, we design different CNN models for CU with different segmentation modes. In the design of CNN model, we use the symmetric convolutional kernel and asymmetric convolutional kernel to extract features in different directions effectively. In the loss function, we used the cross-entropy function to train the CNN model to improve the accuracy of the model. Finally, a double threshold is set in the candidate list to achieve a compromise between coding performance and coding complexity. Experimental results show that, compared to the VTM10.0 anchoring algorithm, our fast scheme, in terms of encoding time, decreases by 55.90% and BDBR increases by 1.79%; our moderate scheme, in terms of encoding time, decreases by 47.90%. BDBR increases by only 1.29%.

No-Reference Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds

No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the information extracted from a bitstream for real-time and nonintrusive quality monitoring. We propose a no-reference bitstream-layer model for the perceptual quality assessment of video-based point cloud compression (V-PCC) encoded point clouds. First, we describe the fundamental relationship between perceptual coding distortion and the texture quantization parameter (TQP) when geometry encoding is lossless. Then, we incorporate the texture complexity (TC) into the proposed model while considering the fact that the perceptual coding distortion of a point cloud depends on the texture characteristics. TC is estimated using TQP and the texture bitrate per pixel (TBPP), both of which are extracted from the compressed bitstream without resorting to complete decoding. Then, we construct a texture distortion assessment model upon TQP and TBPP. By combining this texture distortion model with the geometry quantization parameter (GQP), we obtain an overall no-reference bitstream-layer PCQA model that we call bitstreamPCQ. Experimental results show that the proposed model markedly outperforms existing models in terms of widely used performance criteria, including the Pearson linear correlation coefficient (PLCC), the Spearman rank order correlation coefficient (SRCC) and the root mean square error (RMSE). The dataset developed in this study is publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-3.0.

Bitstream-based Perceptual Quality Assessment of Compressed 3D Point Clouds.

With the increasing demand of compressing and streaming 3D point clouds under constrained bandwidth, it has become ever more important to accurately and efficiently determine the quality of compressed point clouds, so as to assess and optimize the quality-of-experience (QoE) of end users. Here we make one of the first attempts developing a bitstream-based no-reference (NR) model for perceptual quality assessment of point clouds without resorting to full decoding of the compressed data stream. Specifically, we first establish a relationship between texture complexity and the bitrate and texture quantization parameters based on an empirical rate-distortion model. We then construct a texture distortion assessment model upon texture complexity and quantization parameters. By combining this texture distortion model with a geometric distortion model derived from Trisoup geometry encoding parameters, we obtain an overall bitstream-based NR point cloud quality model named streamPCQ. Experimental results show that the proposed streamPCQ model demonstrates highly competitive performance when compared with existing classic full-reference (FR) and reduced-reference (RR) point cloud quality assessment methods with a fraction of computational cost.

Computational assessment of the potential of cross-catalytic coprecipitating systems for the bottom-up design of nanocomposites

Using a computational modelling approach, we study the potential of cross-catalytic coprecipitating systems for the synthesis of nanocomposites. We found that these systems can form self-organized nanocomposites with various complex textures.

Intelligent Typography: Artistic Text Style Transfer for Complex Texture and Structure

Text style transfer is an important task to render artistic texts from a reference image or style, and is widely desired in many visual creations. Previous works have brought some efficient methods for text style transfer, which facilitate users to design various artistic texts automatically. However, these works mainly focus on relatively simple text effects, and do not perform well on complex reference styles. In this paper, we propose a coarse-to-fine framework to generate exquisite texts with complex texture and structure in an unsupervised way, achieving real-time control of style scales (i.e., text stylistic degree or deformation degree). The key idea is to decouple the overall task into two steps, prototype generation and detail refinement, and explore delicate networks for each step to imitate the features at different levels. Based on this idea, in the first step, we present a novel pro-gen GAN to generate prototypes of artistic texts using the reference style, and develop a deformable module to empower the pro-gen GAN to continuously characterize the multi-scale shape features without network retraining. Furthermore, we propose a mix-attention training scheme for text style transfer, which can avoid artifacts and retain a clear text background. In the second step, we introduce two optimized networks for detail refinements. Experimental results show that the proposed method can synthesize exquisite stylized texts with complex reference styles, and surpass the state of the arts in texture reconstruction, contour imitation, and text image quality drastically. The code is available on the page: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WendongMao/Intelligent_Typography</uri> .

Adaptive Backdoor Attack against Deep Neural Networks

In recent years, the number of parameters of deep neural networks (DNNs) has been increasing rapidly. The training of DNNs is typically computation-intensive. As a result, many users leverage cloud computing and outsource their training procedures. Outsourcing computation results in a potential risk called backdoor attack, in which a welltrained DNN would perform abnormally on inputs with a certain trigger. Backdoor attacks can also be classified as attacks that exploit fake images. However, most backdoor attacks design a uniform trigger for all images, which can be easily detected and removed. In this paper, we propose a novel adaptive backdoor attack. We overcome this defect and design a generator to assign a unique trigger for each image depending on its texture. To achieve this goal, we use a texture complexity metric to create a special mask for each image, which forces the trigger to be embedded into the rich texture regions. The trigger is distributed in texture regions, which makes it invisible to humans. Besides the stealthiness of triggers, we limit the range of modification of backdoor models to evade detection. Experiments show that our method is efficient in multiple datasets, and traditional detectors cannot reveal the existence of a backdoor.

Rational design of intergrowth P2/O3 biphasic layered structure with reversible anionic redox chemistry and structural evolution for Na-ions batteries

Layered oxides have attracted unprecedented attention for their outstanding performance in sodium-ion battery cathodes. Among them, the two typical candidates P2 and O3 type materials generally demonstrate large diversities in specific capacity and cycling endurance with their advantages. Thus, composite materials that contain both P2 and O3 have been widely designed and constructed. Nevertheless, the anionic/cationic ions’ behavior and structural evolution in such complex structures remain unclear. In this study, a deep analysis of an advanced Na0.732Ni0.273Mg0.096Mn0.63O2 material that contains 78.39 wt% P2 phase and 21.61 wt% O3 phase is performed based on two typical cathodes P2 Na0.67Ni0.33Mn0.67O2 and O3 NaNi0.5Mn0.5O2 that have the same elemental constitution but different crystal structures. Structural analysis and density functional theory (DFT) calculations suggest that the composite is preferred to form a symbiotic structure at the atomic level, and the complex lattice texture of the biphase structure can block unfavorable ion and oxygen migration in the electrode process. Consequently, the biphase structure has significantly improved the electrochemical performance and kept preferable anionic oxygen redox reversibility. Furthermore, the hetero-epitaxy-like structure of the intergrowth of P2 and O3 structures share multi-phase boundaries, where the inconsistency in electrochemical behavior between P2 and O3 phases leads to an interlocking effect to prevent severe structural collapse and relieves the lattice strain from Na+ de/intercalation. Hence, the symbiotic P2/O3 composite materials exhibited a preferable capacity and cyclability (∼130 mAh g−1 at 0.1 C, 73.1% capacity retention after 200 cycles at 1 C), as well as reversible structural evolution. These findings confirmed the advantages of using the bi/multi-phase cathode for high-energy Na-ion batteries.