Texture Information Research Articles

A video object detector with Spatio-Temporal Attention Module for micro UAV detection

Many deep neural network-based methods have recently been proposed for object detection due to the significant success of deep learning in computer vision. However, existing object detection methods typically extract the appearance features of objects from single image so that they usually suffer from poor performance in detecting micro Unmanned Aerial Vehicle (UAV), because micro UAV lacks of rich color, shape and texture information. To address this issue, we introduce the temporal information of objects from videos and develop a Spatio-Temporal Attention Module (STAM) to efficiently enhance feature map extraction for detecting micro UAV, and then integrate STAM into YOLOX to develop a video object detector for micro UAV. Meanwhile, we propose a lightweight Spatial Pyramid Pooling (SPP) module termed Group Simplified Spatial Pyramid Pooling-Fast with Cross Stage Partial (Group SimSPPFCSP) for the backbone’s final stage layer to efficiently and lightly extract more semantic information, and we propose a neck with rich propagation pathways (NRPP) to facilitate the effective propagation of spatial and temporal information across different levels. Furthermore, we propose two data augmentation operations including SeqMosaic and SeqMixUp, to augment video data for video object detection. Experimental results show that our model can achieve competitive precision (with 5.0 mAP and 8.1 mAPSmall improvement) while maintaining real-time inference speed (35.3 fps).

Effect of Inoculation with Autochthonous Lactic Acid Bacteria on Flavor, Texture, and Color Formation of Dry Sausages with NaCl Partly Substituted by KCl.

The effects of inoculating lactic acid bacteria (LAB), specifically Lactiplantibacillus plantarum, Latilactobacillus sakei, Latilactobacillus curvatus, and Weissella hellenica on the flavor, texture, and color formation of dry sausages in which NaCl was partially substituted by 40% KCl, were explored in this study. It was found that LAB inoculation increased the presence of ketones, alcohols, acids, esters, and terpenes. It also reduced the pH, moisture, protein, and fat content, improving the b*-value, flavor, and texture of the sausages. Notably, L. sakei inoculation showed the most significant improvement in dry sausages with NaCl substitutes, especially on the reduction of bitterness. Meanwhile, there was a close positive correlation between the LAB count with the alcohols and esters formation of dry sausage with NaCl substitution (p < 0.05). These findings offer insight into improving the product characteristics of dry sausages using NaCl substitutes.

Dual-branch network based on transformer for texture recognition

Texture recognition is the task of identifying and understanding the patterns and structures present in textures within digital images, constituting a highly challenging research topic. With the rapid advancement of deep learning, CNN-based methods have progressively supplanted traditional approaches, emerging as the predominant techniques for texture recognition. Nevertheless, these methods face limitations in capturing the structural information of textures from global features, attributed to the constraints of CNNs. Structural information stands out as a crucial factor in describing and recognizing real-world texture images. To address this challenge, we propose DBTrans, a dual-branch network based on Transformer architecture for texture recognition. DBTrans leverages the transformer's extraction capability for global features (low-frequency information) to capture the structural nuances of texture images. Additionally, recognizing the distinctive nature of texture recognition tasks, we propose a Transformer variant module tailored for this purpose, named Residual Pooling Transformer (RPT). Finally, we investigate the impact of different branches and modules on network performance and showcase the performance of DBTrans on five public datasets, achieving state-of-the-art accuracy.

A simple and robust method for laboratory-scale preparation of butter

The aim of this study was to develop a small-scale model system resembling the micro- and meso-structure of butter, namely having a water-droplet size distribution and water content close to that of commercially produced butter. Although it is possible to churn cream on a small scale, matching the microstructure of butter is a challenge. A 2-step churning process was introduced with the application of a kitchen mixer. The resulting microstructure was evaluated using confocal laser scanning microscopy. In addition, low-field nuclear magnetic resonance was used to determine the water-droplet size distribution. Results demonstrated that a water content of 16–19% could be obtained with the proposed procedure, close to the standard water content of 16%. Average water droplet size ranged between 2.5 to 4.3 µm and did not depend on batch-to-batch variations, nor storage-induced differences in the cream. In conclusion, the proposed method could be employed to prepare water-in-oil emulsions with a microstructure similar to that of butter and opens new opportunities for studying microbial growth, flavor release, and texture formation.

Neural Radiance Selector: Find the best 2D representations of 3D data for CLIP based 3D tasks

Representing the world in 3D space provides vivid texture and depth information. However, 3D datasets currently do not match the scale of 2D datasets. There is a growing trend in representing 3D data as multi-view 2D images and using large-scale 2D models, to solve 3D tasks. In this work, we present the Neural Radiance Selector, a method that automatically selects the optimal 2D representations of 3D data. Instead of indiscriminately sampling multi-view 2D images, we define the optimal 2D views as those capable of reconstructing the entire 3D scene with a conditional neural radiance field. We propose two distinct methods for 3D point cloud data and 3D implicit models to achieve faster inference. We demonstrate the efficacy of our methods in various 3D tasks, including zero-shot 3D point cloud classification, 3D implicit model classification, and language-guided NeRF editing.

CAA: Class-Aware Affinity calculation add-on for semantic segmentation

Leveraging contextual dependencies is a commonly used technique to enhance the performance of image segmentation. However, existing solutions do not effectively catch the class-level association between the pixels along the boundary across the objects of the different classes but focus more on the local pixel-to-pixel relation. This work proposes a Class-Aware Affinity module (CAA) that considers both pixel-to-pixel relation and pixel-to-class association. We try to argue that the pixel-to-pixel relations still catch the relation (e.g. similarity, attention, or affiliation) on the local texture level. At the same time, it should also consider the association between the pixel and the class context produced by the given image. Pixel-to-class association can best reveal the co-occurrent dependency on the semantic level between the given pixels and their nearby context. Such pixel-to-class association combined with the pixel-to-pixel relations aggregating the local texture information will best mitigate the confusion caused in the boundary regions across the objects of the different classes. Moreover, the proposed framework can serve as a generic add-on to be integrated with the existing image segmentation solution to boost the current performance. Equipped with CAA, we achieve promising performance against the existing work with 54.59% mIoU on ADE20K, 49.96% mIoU on COCO-Stuff10k, and 64.38% mIoU on Pascal-Context.

Lipidomics analysis reveals new insights into crisp grass carp associated with meat texture

Feeding faba beans to grass carp could crisp its muscle texture to avoid softening, the relationship between texture formation throughout the crisping process and the critical lipids regulating the fish quality has not yet been clarified. Herein, an 60-day nutritional trial and untargeted lipidomic analysis was used to study the changes of lipids in crisp grass carp dorsal muscle. A total of 1036 lipids were remarkably different between ordinary and crisp grass carp. The concentrations of the LPC, LPE, PG, Cer, Hex2Cer, SM, MG and MGMG were positively correlated with hardness and springiness, and the CL, TG, PMe, WE, dMePE and AcCa were negative correlation. High content of lipids involved in storage in ordinary grass carp, such as glycerophospholipids, polyunsaturated and saturated fatty acid content. In contrast, high content of membrane components in crisp grass carp, such as monounsaturated fatty acid, sphingolipid and glycerolipids content, and the distribution of PUFA in lipid molecules was related to lipid biosynthesis. This study might provide some insights into improved knowledge of the association between meat texture and lipid molecules in fish fed with faba bean.

DCP-Net: An Efficient Image Segmentation Model for Forest Wildfires

Wildfires usually lead to a large amount of property damage and threaten life safety. Image recognition for fire detection is now an important tool for intelligent fire protection, and the advancement of deep learning technologies has enabled an increasing number of cameras to possess functionalities for fire detection and automatic alarm triggering. To address the inaccuracies in extracting texture and positional information during intelligent fire recognition, we have developed a novel network called DCP-Net based on UNet, which excels at capturing flame features across multiple scales. We conducted experiments using the Corsican Fire Dataset produced by the “Environmental Science UMR CNRS 6134 SPE” laboratory at the University of Corsica and the BoWFire Dataset by Chino et al. Our algorithm was compared with networks such as SegNet, UNet, UNet++, and PSPNet, demonstrating superior performance across three metrics: mIoU, F1-score, and OA. Our proposed deep learning model achieves the best mIoU (78.9%), F1-score (76.1%), and OA (96.7%). These results underscore the robustness of our algorithm, which accurately identifies complex flames, thereby making a significant contribution to intelligent fire recognition. Therefore, the proposed DCP-Net model offers a viable solution to the challenges of wildfire monitoring using cameras, with hardware and software requirements typical of deep learning setups.

Multi-channel and multi-featured extended orb with gabor filter and non-maximum suppression

This research paper presents an enhanced approach to feature extraction using the Oriented FAST and Rotated BRIEF (ORB) algorithm (Rublee et al., 2014). The proposed method leverages the power of Gabor filters (Mehrotra et al., 1992) in combination with Non-Maximum Suppression (Neuback and Van Gool, 2006) to improve the robustness and efficiency of feature detection in computer vision applications. The process begins with the acquisition of an RGB image, which is then transformed into seven single-channel images representing different color and intensity aspects: red, green, blue, hue, saturation, value, and grayscale. Each single-channel image is independently subjected to Gabor filtering, resulting in seven Gabor-filtered images. These images capture distinct texture and frequency information, enhancing the discriminative power of subsequent feature extraction. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) algorithm is applied to each of the seven Gabor-filtered images to extract key points and descriptors. This step yields a comprehensive set of keypoints (Mallick et al., 2015) and descriptors, capturing rich local feature information across multiple image channels. To further refine the extracted features and eliminate redundant keypoints, Non-Maximum Suppression is employed independently on each single-channel image. This process effectively filters out overlapped and false keypoints, ensuring the selection of only the most salient features for downstream tasks. Experimental results demonstrate the efficacy of the proposed approach in improving feature detection performance compared to traditional methods. The combination of Gabor filtering and Non-Maximum Suppression enhances feature discriminability, robustness to noise, and resistance to image transformations, making it well-suited for various computer vision applications, including object recognition, image matching, and scene understanding.

Remote sensing image dehazing using generative adversarial network with texture and color space enhancement

Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

Densely integrated multi-branch attentive net for image dehazing

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing.

EDUNet++: An Enhanced Denoising Unet++ for Ice-Covered Transmission Line Images

New technology has made it possible to monitor and analyze the condition of ice-covered transmission lines based on images. However, the collected images are frequently accompanied by noise, which results in inaccurate monitoring. Therefore, this paper proposes an enhanced denoising Unet++ for ice-covered transmission line images (EDUNet++). This algorithm mainly comprises three modules: a feature encoding and decoding module (FEADM), a shared source feature fusion module (SSFFM), and an error correction module (ECM). In the FEADM, a residual attention module (RAM) and a multilevel feature attention module (MFAM) are proposed. The RAM incorporates the cascaded residual structure and hybrid attention mechanism, that effectively preserve the mapping of feature information. The MFAM uses dilated convolution to obtain features at different levels, and then uses feature attention for weighting. This module effectively combines local and global features, which can better capture the details and texture information in the image. In the SSFFM, the source features are fused to preserve low-frequency information like texture and edges in the image, hence enhancing the realism and clarity of the image. The ECM utilizes the discrepancy between the generated image and the original image to effectively capture all the potential information in the image, hence enhancing the realism of the generated image. We employ a novel piecewise joint loss. On the dataset of ice-covered transmission lines, PSNR (peak signal to noise ratio) and SSIM (structural similarity) achieved values of 29.765 dB and 0.968, respectively. Additionally, the visual effects exhibited more distinct detailed features. The proposed method exhibits superior noise suppression capabilities and robustness compared to alternative approaches.

Infrared Small Target Detection Based on Density Peak Search and Local Features

The detection of small infrared targets is still a challenging task and efficient and accurate detection plays a key role in modern infrared search and tracking military applications. However, small infrared targets are difficult to detect due to their weak brightness, small size and lack of shape, structure, texture, and other information elements. In this paper, we propose a target detection method. First, to address the problem that the proximity of targets to high-brightness clutter leads to missed detection of candidate targets, a Gaussian differential filtering preprocessed image is used to suppress high-brightness clutter. Second, a density-peaked global search method is used to determine the location of candidate targets in the preprocessed image. We then use local contrast to the candidate target points to enhance the gradient features and suppress background clutter. The Facet model is used to compute multidirectional gradient features at each point. A new efficient surrounding symmetric region partitioning scheme is constructed to capture the gradient characteristics of targets of different sizes in eight directions, followed by weighting the candidate target gradient characteristics using the standard deviation of the symmetric region difference. Finally, an adaptive threshold segmentation method is used to extract small targets. Experimental results show that the method proposed in this paper has better detection accuracy and robustness compared with other detection methods.

Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments

When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency.

Activated Sparsely Sub-Pixel Transformer for Remote Sensing Image Super-Resolution

Transformers have recently achieved significant breakthroughs in various visual tasks. However, these methods often overlook the optimization of interactions between convolution and transformer blocks. Although the basic attention module strengthens the feature selection ability, it is still weak in generating superior quality output. In order to address this challenge, we propose the integration of sub-pixel space and the application of sparse coding theory in the calculation of self-attention. This approach aims to enhance the network’s generation capability, leading to the development of a sparse-activated sub-pixel transformer network (SSTNet). The experimental results show that compared with several state-of-the-art methods, our proposed network can obtain better generation results, improving the sharpness of object edges and the richness of detail texture information in super-resolution generated images.

ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

AbstractThe colour‐enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER‐Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER‐Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold‐based residual selection in LiDAR‐inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

Diagnostic significance of multisequence MRI radiomics models in distinguishing benign and malignant spinal fractures

PurposeTo investigate the diagnostic value of multi-sequence magnetic resonance imaging (MRI) in benign and malignant spinal fractures. Materials and methodsMRI data of patients with pathologically confirmed malignant vertebral fractures were retrospectively collected and compared with those with benign vertebral fractures. The image omics features of T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) were extracted respectively, and were randomly divided into training and test sets at 8:2. The training set adopted recursive feature elimination method and minimum absolute contraction and selection operator (LASSO) regression to screen the variables. Three image omics models, T1WI, T2WI and the combination of two sequences, were constructed by logistic regression, and the diagnostic efficiency of each model was verified by the test set. The receiver operating curve evaluated the diagnostic efficiency of the model. ResultsA total of 111 vertebrae with fracture were included in 97 patients, 55 with benign fracture and 56 with malignant fracture. In the training set, the AUC, sensitivity and specificity of the T1WI model were 0.892, 88.9% and 81.4%, respectively. In T2WI model, the results were 0.924, 82.2% and 95.3%, respectively. The combined models of the two sequences were 0.934, 90.0% and 87.5%, respectively. In the test set, the AUC, sensitivity and specificity of T1WI model were 0.877, 80.0% and 92.3%, respectively. In T2WI model, the results were 0.923, 90.0% and 92.3%, respectively. The combined models were 0.933, 100.0% and 87.5%, respectively. The diagnostic efficiency of the two-sequence combined image omics model was better than that of the radiologist. ConclusionThe combined model, incorporating T1WI and T2WI, outperformed single-sequence models in terms of diagnostic accuracy. The integration of multisequence radiomic features provided enhanced texture information, contributing to superior accuracy and efficiency in diagnosing malignant spinal fractures.

A Study on the Effect Mechanism of Pectin Modification on the Carrot Cell Wall’s Texture Formation under Ultrasonic and Infrared Drying

The carrot has a high water content, and dehydration is an important means to extend its edible period and reduce storage and transportation costs. In the case of infrared (IR) drying, the porosity of the product is low and the structure is compact; the textural properties of the product are improved by using combined ultrasound and infrared (US-IR) drying; however, there is a lack of reports on the mechanism of this. Pectin has an important influence on the formation of the textural properties of fruit and vegetable tissues. In order to investigate the mechanism of the change in endogenous pectin properties in the carrot cell wall under US-IR drying on the improvement of the textural properties of the product, different fractions of pectins (water-soluble pectin, chelating pectin, alkali-soluble pectin) of the carrot were extracted, separated, and analysed. The thermal stability, component and content changes, Fourier infrared (FTIR), X-ray diffraction (XRD), esterification degree, molecular weight, monosaccharide composition, Ca ion content, and atomic force microscopy (AFM) of the pectins were determined. The results showed that the changes in the contents and properties of the carrot pectins under US-IR conditions had a positive effect on the improvement of the textural properties of the carrot tissues.

Reinforced Res-Unet transformer for underwater image enhancement

Light propagation through water is subject to varying degrees of energy loss, causing captured images to display characteristics of color distortion, reduced contrast, and indistinct details and textures. The data-driven approach offers significant advantages over traditional algorithms, such as improved accuracy and reduced computational costs. However, challenges such as optimizing network architecture, refining coding techniques, and expanding database resources must be addressed to ensure the generation of high-quality reconstructed images across diverse tasks. In this paper, an underwater image enhancement network based on feature fusion is proposed named RUTUIE, which integrates feature fusion techniques. It leverages the strengths of both Resnet and U-shape architecture, primarily structured around a streamlined up-and-down sampling mechanism. Specifically, the U-shaped structure serves as the backbone of ResNet, equipped with two feature transformers at both the encoding and decoding ends, which are linked by a single-stage up-and-down sampling structure. This architecture is designed to minimize the omission of minor features during feature scale transformations. Furthermore, the improved Transformer encoder leverages a feature-level attention mechanism and the advantages of CNNs, endowing the network with both local and global perceptual capabilities. Then, we propose and demonstrate that embedding an adaptive feature selection module at appropriate locations can retain more learned feature representations. Moreover, the application of a previously proposed color transfer method for synthesizing underwater images and augmenting network training. Extensive experiments demonstrate that our work effectively corrects color casts, reconstructs the rich texture information in natural scenes, and improves the contrast.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.