Diversity Of Textures Research Articles

A New Approach for Super Resolution Object Detection Using an Image Slicing Algorithm and the Segment Anything Model.

Object detection in high resolution enables the identification and localization of objects for monitoring critical areas with precision. Although there have been improvements in object detection at high resolution, the variety of object scales, as well as the diversity of backgrounds and textures in high-resolution images, make it challenging for detectors to generalize successfully. This study introduces a new method for object detection in high-resolution images. The pre-processing stage of the method includes ISA and SAM to slice the input image and segment the objects in bounding boxes, respectively. In order to improve the resolution in the slices, the first layer of YOLO is designed as SRGAN. Thus, before applying YOLO detection, the resolution of the sliced images is increased to improve features. The proposed system is evaluated on xView and VisDrone datasets for object detection algorithms in satellite and aerial imagery contexts. The success of the algorithm is presented in four different YOLO architectures integrated with SRGAN. According to comparative evaluations, the proposed system with Yolov5 and Yolov8 produces the best results on xView and VisDrone datasets, respectively. Based on the comparisons with the literature, our proposed system produces better results.

Symmetrization of quasi-regular patterns with periodic tilting of regular polygons

AbstractComputer-generated aesthetic patterns are widely used as design materials in various fields. The most common methods use fractals or dynamical systems as basic tools to create various patterns. To enhance aesthetics and controllability, some researchers have introduced symmetric layouts along with these tools. One popular strategy employs dynamical systems compatible with symmetries that construct functions with the desired symmetries. However, these are typically confined to simple planar symmetries. The other generates symmetrical patterns under the constraints of tilings. Although it is slightly more flexible, it is restricted to small ranges of tilings and lacks textural variations. Thus, we proposed a new approach for generating aesthetic patterns by symmetrizing quasi-regular patterns using general k-uniform tilings. We adopted a unified strategy to construct invariant mappings for k-uniform tilings that can eliminate texture seams across the tiling edges. Furthermore, we constructed three types of symmetries associated with the patterns: dihedral, rotational, and reflection symmetries. The proposed method can be easily implemented using GPU shaders and is highly efficient and suitable for complicated tiling with regular polygons. Experiments demonstrated the advantages of our method over state-of-the-art methods in terms of flexibility in controlling the generation of patterns with various parameters as well as the diversity of textures and styles.

Laser‐Induced Real‐Space Topology Control of Spin Wave Resonances

AbstractFemtosecond laser excitation of materials exhibiting magnetic spin textures promises advanced magnetic control via the generation of non‐equilibrium spin dynamics. Ferrimagnetic [Fe(0.35 nm)/Gd(0.40 nm)]160 multilayers are used to explore this approach, as they host a rich diversity of magnetic textures from stripe domains at low magnetic fields, a dense bubble/skyrmion lattice at intermediate fields, and a single domain state for high magnetic fields. Using femtosecond magneto‐optics, distinct coherent spin wave dynamics are observed in this material in response to a weak laser excitation, enabling an unambiguous identification of the different magnetic spin textures. Moreover, employing strong laser excitation, versatile control of the coherent spin dynamics via non‐equilibrium transformation of magnetic spin textures becomes possible by both creating and annihilating bubbles/skyrmions. Micromagnetic simulations and Lorentz transmission electron microscopy with in situ optical excitation corroborate these findings.

Active learning strategies for robotic tactile texture recognition tasks.

Accurate texture classification empowers robots to improve their perception and comprehension of the environment, enabling informed decision-making and appropriate responses to diverse materials and surfaces. Still, there are challenges for texture classification regarding the vast amount of time series data generated from robots' sensors. For instance, robots are anticipated to leverage human feedback during interactions with the environment, particularly in cases of misclassification or uncertainty. With the diversity of objects and textures in daily activities, Active Learning (AL) can be employed to minimize the number of samples the robot needs to request from humans, streamlining the learning process. In the present work, we use AL to select the most informative samples for annotation, thus reducing the human labeling effort required to achieve high performance for classifying textures. We also use a sliding window strategy for extracting features from the sensor's time series used in our experiments. Our multi-class dataset (e.g., 12 textures) challenges traditional AL strategies since standard techniques cannot control the number of instances per class selected to be labeled. Therefore, we propose a novel class-balancing instance selection algorithm that we integrate with standard AL strategies. Moreover, we evaluate the effect of sliding windows of two-time intervals (3 and 6 s) on our AL Strategies. Finally, we analyze in our experiments the performance of AL strategies, with and without the balancing algorithm, regarding f1-score, and positive effects are observed in terms of performance when using our proposed data pipeline. Our results show that the training data can be reduced to 70% using an AL strategy regardless of the machine learning model and reach, and in many cases, surpass a baseline performance. Finally, exploring the textures with a 6-s window achieves the best performance, and using either Extra Trees produces an average f1-score of 90.21% in the texture classification data set.

Potential of Ecoenzymes and N2 Nanobubbles on the Growth of Phalaenopsis sp. Orchid at the Acclimatization Stage

Growth is a quantitative change during one plant’s life cycle and is irreversible.withaddition An in crease the volume or dry weight of plants or other organs is due to the adding, of new structural elements. Phalaenopsis (moth orchid) is a genus of orchids appeal. The diversity of colors, shapes, and textures, as well as its aroma, make Phalaenopsis orchids one of the charming flowers of Indonesia. Ecoenzyme is a solution of complex organic substances produced from the fermentation of organic residues, sugar, and water. Nanobubbles (NBs) are an example of nanotechnology. NBs have a diameter of 1-100 nm and are nanoscopic gas bubbles in a solution or water that can change the characteristics of water and are stable. This study aims to determine the potential of giving Ecoenzyme and NBs N2 on the growth of Phalaenopsis orchids during the acclimatization stage. This research was conducted from October to December 2022 at the Orchidology and Nursery Laboratory, Islamic University of Malang. This study used an experimental method with a completely randomized design (CRD) with 10 treatments, namely 0 mL/L (control), 1 mL/L Ecoenzyme, 2 mL/L Ecoenzyme, 3 mL/L Ecoenzyme, 4 mL/L Ecoenzyme, 4 combination NBs N2 and Ecoenzymes, and 5 mL NBs N2 with 3 replications, making a total of 30 treatment units. The research parameters included the number of leaves, leaf length, number of roots, root length, plant height, fresh weight, and plant dry weight. The results of this study showed that the treatment of 3 mL/L Ecoenzyme showed potential in increasing the number of leaves by 4 pieces: at the same time the optimal concentration of the combination of Ecoenzyme and NBs was 2 mL/L plus 5 mL NBs N2 in increasing the number of roots by 10 pieces, and the optimal concentration of NBs was 5 mL in increasing the root length by 7.07 cm, plant height by 17.10 cm leaf. length, plant weight by 4.16 g and dry weight by 0.85 g in Phalaenopsis orchids.

Diversity of radial spin textures in chiral materials

Diversity of radial spin textures in chiral materials

Graph- and Machine-Learning-Based Texture Classification

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification.

Biochemistry of aroma compounds in cheese

Cheese is one of the most popular dairy products, with an incredible diversity of varieties, shapes, textures, and flavours available locally and globally. Extensive research has been conducted on the aroma compounds present in cheese. Numerous studies have identified over 3000 volatile and non-volatile components in cheese. The volatile components, which consist of fatty acids, alcohols, aldehydes, esters, ketones, lactones, sulphur compounds, amines, and other flavour-active compounds, are primarily responsible for the aromatic characteristics (aroma) of cheese. This paper thoroughly discusses the biochemistry behind the formation of significant aroma compounds that affect overall cheese quality and sensory characteristics. It provides an extensive review of the most recent methodologies for the extraction and determination of aroma compounds in cheese.

A Mars 2020 Perseverance SuperCam Perspective on the Igneous Nature of the Máaz Formation at Jezero Crater and Link With Séítah, Mars

AbstractThe Máaz formation consists of the first lithologies in Jezero crater analyzed by the Mars 2020 Perseverance rover. This formation, investigated from Sols (Martian days) 1 to 201 and from Sols 343 to 382, overlies the Séítah formation (previously described as an olivine‐rich cumulate) and was initially suggested to represent an igneous crater floor unit based on orbital analyses. Using SuperCam data, we conducted a detailed textural, chemical, and mineralogical analyses of the Máaz formation and the Content member of the Séítah formation. We conclude that the Máaz formation and the Content member are igneous and consist of different lava flows and/or possibly pyroclastic flows with complex textures, including vesicular and non‐vesicular rocks with different grain sizes. The Máaz formation rocks exhibit some of the lowest Mg# (=molar 100 × MgO/MgO + FeO) of all Martian igneous rocks analyzed so far (including meteorites and surface rocks) and show similar basaltic to basaltic‐andesitic compositions. Their mineralogy is dominated by Fe‐rich augite to possibly ferrosilite and plagioclase, and minor phases such as Fe‐Ti oxides and Si‐rich phases. They show a broad diversity of both compositions and textures when compared to Martian meteorites and other surface rocks. The different Máaz and Content lava or pyroclastic flows all originate from the same parental magma and/or the same magmatic system, but are not petrogenetically linked to the Séítah formation. The study of returned Máaz samples in Earth‐based laboratories will help constrain the formation of these rocks, calibrate Martian crater counting, and overall, improve our understanding of magmatism on Mars.

Heliknoton in a film of cubic chiral magnet

Cubic chiral magnets exhibit a remarkable diversity of two-dimensional topological magnetic textures, including skyrmions. However, the experimental confirmation of topological states localized in all three spatial dimensions remains challenging. In this paper, we investigate a three-dimensional topological state called a heliknoton, which is a hopfion embedded into a helix or conic background. We explore the range of parameters at which the heliknoton can be stabilized under realistic conditions using micromagnetic modeling, harmonic transition state theory, and stochastic spin dynamics simulations. We present theoretical Lorentz TEM images of the heliknoton, which can be used for experimental comparison. Additionally, we discuss the stability of the heliknoton at finite temperatures and the mechanism of its collapse. Our study offers a pathway for future experimental investigations of three-dimensional topological solitons in magnetic crystals.

Fast cancer metastasis location based on dual magnification hard example mining network in whole-slide images

Breast cancer has become the most common form of cancer among women. In recent years, deep learning has shown great potential in aiding the diagnosis of pathological images, particularly through the use of convolutional neural networks for locating lymph node metastasis under gigapixel whole slide images (WSIs). However, the massive size of these images at the highest magnification introduces redundant computation during the inference process. Additionally, the diversity of biological textures and structures within WSIs can cause confusion for classifiers, particularly in identifying hard examples. As a result, the trade-off between accuracy and efficiency remains a critical issue for whole-slide image metastasis localization. In this paper, we propose a novel two-stream network that takes a pair of low- and high-magnification image patches as input for identifying hard examples during the training phase. Specifically, our framework focuses on samples where the outputs of the two magnification networks are dissimilar. We adopt a dual magnification hard mining loss to re-weight the ambiguous samples. To more efficiently locate tumor metastasis cells in whole slide images, the two stream networks are decomposed into a cascaded network during the inference phase. The low magnification WSIs scanned by the low-mag network generate a coarse probability map, and the suspicious areas in the map are refined by the high-mag network. Finally, we evaluate our fast location dual magnification hard example mining network on the Camelyon16 breast cancer whole-slide image dataset. Experiments demonstrate that our proposed method achieves a 0.871 FROC score with a faster inference time, and our high magnification network also achieves a 0.88 FROC score.

Toward universal texture synthesis by combining texton broadcasting with noise injection in StyleGAN-2

We present a universal texture synthesis approach that incorporates a novel multiscale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of a broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset, NUUR-Texture500, that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space.

Multi-surface defect detection for universal joint bearings via multimodal feature and deep transfer learning

ABSTRACT Surface defect detection by machine vision has received increased attention concerning the quality control of universal joint bearings (UJB). The defect distribution and counting information are important for product quality optimisation. However, vision defect detection for UJB remains a challenging task due to the diversity of background textures and defect characters on multiple surfaces. In this study, a multi-surface defect detection (MSDD) method consisting of region segmentation, feature extraction and detection is proposed. First, defect regions are accurately localised by the proposed adaptive defect region segmentation algorithm, which suppresses the inference of background variety. Then, a novel defect feature named multimodal fusion shape descriptor that integrates the global information and local information of defects is constructed to generate the discriminative defect representation. Finally, a defect feature extraction ability transfer strategy based on the transfer learning mechanism is proposed to address the problem of insufficient defect samples. The experimental results show that our method achieves the best accuracy of 94.8% macro-F1 and processes 28 defect images per second. Besides, the application effect in the practical production line indicates that our method meets the accuracy and real-time requirements of MSDD for UJB.

CMANet: Cross-Modality Attention Network for Indoor-Scene Semantic Segmentation.

Indoor-scene semantic segmentation is of great significance to indoor navigation, high-precision map creation, route planning, etc. However, incorporating RGB and HHA images for indoor-scene semantic segmentation is a promising yet challenging task, due to the diversity of textures and structures and the disparity of multi-modality in physical significance. In this paper, we propose a Cross-Modality Attention Network (CMANet) that facilitates the extraction of both RGB and HHA features and enhances the cross-modality feature integration. CMANet is constructed under the encoder-decoder architecture. The encoder consists of two parallel branches that successively extract the latent modality features from RGB and HHA images, respectively. Particularly, a novel self-attention mechanism-based Cross-Modality Refine Gate (CMRG) is presented, which bridges the two branches. More importantly, the CMRG achieves cross-modality feature fusion and produces certain refined aggregated features; it serves as the most crucial part of CMANet. The decoder is a multi-stage up-sampled backbone that is composed of different residual blocks at each up-sampling stage. Furthermore, bi-directional multi-step propagation and pyramid supervision are applied to assist the leaning process. To evaluate the effectiveness and efficiency of the proposed method, extensive experiments are conducted on NYUDv2 and SUN RGB-D datasets. Experimental results demonstrate that our method outperforms the existing ones for indoor semantic-segmentation tasks.

3D Reconstruction of Remote Sensing Mountain Areas with TSDF-Based Neural Networks

The remote sensing 3D reconstruction of mountain areas has a wide range of applications in surveying, visualization, and game modeling. Different from indoor objects, outdoor mountain reconstruction faces additional challenges, including illumination changes, diversity of textures, and highly irregular surface geometry. Traditional neural network-based methods that lack discriminative features struggle to handle the above challenges, and thus tend to generate incomplete and inaccurate reconstructions. Truncated signed distance function (TSDF) is a commonly used parameterized representation of 3D structures, which is naturally convenient for neural network computation and computer storage. In this paper, we propose a novel deep learning method with TSDF-based representations for robust 3D reconstruction from images containing mountain terrains. The proposed method takes in a set of images captured around an outdoor mountain and produces high-quality TSDF representations of the mountain areas. To address the aforementioned challenges, such as lighting variations and texture diversity, we propose a view fusion strategy based on reweighted mechanisms (VRM) to better integrate multi-view 2D features of the same voxel. A feature enhancement (FE) module is designed for providing better discriminative geometry prior in the feature decoding process. We also propose a spatial–temporal aggregation (STA) module to reduce the ambiguity between temporal features and improve the accuracy of the reconstruction surfaces. A synthetic dataset for reconstructing images containing mountain terrains is built. Our method outperforms the previous state-of-the-art TSDF-based and depth-based reconstruction methods in terms of both 2D and 3D metrics. Furthermore, we collect real-world multi-view terrain images from Google Map. Qualitative results demonstrate the good generalization ability of the proposed method.

Combined Features in Region of Interest for Brain Tumor Segmentation

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.

What Happens Between Depositional Events, Stays Between Depositional Events: The Significance of Organic Mat Surfaces in the Capture of Ediacara Communities and the Sedimentary Rocks That Preserve Them

In the absence of complex, bioturbating organisms, the seafloor during the Precambrian was covered in widespread organic matgrounds. The greatest diversity and complexity of organic mat textures occur in the Ediacaran fossil record as exemplified by the Ediacara Member of the Rawnsley Quartzite, which crops out in and around the Flinders Ranges, South Australia. This succession unambiguously demonstrates that heterogenous mats coexisted with and were central to the ecology and biology of the Ediacara Biota. Excavation of 33 fossiliferous beds with varying types and extents of organosedimentary surface textures provide the opportunity to utilize this record to develop criteria to evaluate the maturity or extent of growth of Ediacaran matgrounds and, using these characteristics, to examine the relationship between mat type, mat maturity and Ediacara Biota community structure. Based on the assumption that mat maturity represents an indicator of the duration of time between burial events, we can test predictions about the relationship between mat maturity and community development. We find that mat maturity, rather than the mat type itself, more strongly influenced the distribution of taxa and the development of Ediacara macroorganism communities. Using a ranked Mat Maturity Index, we find that although density of macroscopic body fossils and genus diversity correlate with mat maturity, evenness does not. We additionally find that the sessile taxa Obamus and Coronacollina are restricted to surfaces with mature mats whereas all other Ediacaran macrobiota show no connection to mat occurrence and maturity. However, we do observe that large Dickinsonia are more likely to occur on surfaces recording mature matgrounds. The exceptional record of mat surfaces preserved in the Flinders Ranges area demonstrates that, in addition to the apparent ecological role played by mat surfaces in Ediacaran communities, they were also likely a significant component of the Ediacara Member biomass and were integral to community function.

Phase shift in skyrmion crystals

The magnetic skyrmion crystal is a periodic array of a swirling topological spin texture. Since it is regarded as an interference pattern by multiple helical spin density waves, the texture changes with the relative phase shifts among the constituent waves. Although such a phase degree of freedom is relevant to not only magnetism but also transport properties, its effect has not been elucidated thus far. We here theoretically show that a phase shift in the skyrmion crystals leads to a tetra-axial vortex crystal and a meron-antimeron crystal, both of which show a staggered pattern of the scalar spin chirality and give rise to nonreciprocal transport phenomena without the spin-orbit coupling. We demonstrate that such a phase shift can be driven by exchange interactions between the localized spins, thermal fluctuations, and long-range chirality interactions in spin-charge coupled systems. Our results provide a further diversity of topological spin textures and open a new field of emergent electromagnetism by the phase shift engineering.

Development of Novel Forms of Fungal Art Using Aspergillus nidulans.

Fungi are embedded in human culture, tradition, and art, and have featured as inspirational and visual motifs. Psychedelic and medicinal mushrooms have been sculpted, painted, and ingested by our ancestors since prehistory. In modern times, the growing divide between the arts and sciences has delegated fungal art to a niche activity, with the bulk of the focus being on mycelium as a biomaterial. A collaboration between a multidisciplinary artist and a research laboratory, specializing in the molecular study of Aspergillus molds, has allowed us to develop new forms of mycelial art. We describe in detail the development of fungal art techniques using nutrient-rich agar containing Aspergillus nidulans conidia spotted on glass acrylic surfaces or impregnated onto etched acrylic blocks. This approach generates visually and temporally dynamic artwork that is user-friendly, safe, relatively resistant to contamination and easily scalable. Moreover, it offers countless avenues of artistic development based on the diversity of colors, textures and shapes afforded by different fungal species.

Estimation of low-cycle fatigue damage of sputtered Cu thin films at the micro scale using deep learning

Herein, we propose a method to estimate fatigue damage of a thin metal film at the micro scale with high accuracy. With progress in the miniaturization of flexible printed circuits, estimation of damage at the micro scale is required to improve the reliability of industrial processing. Despite this necessity, it has been difficult to accurately evaluate the damage at the micro scale using conventional tests. Therefore, we focus on the use of microscopic images for damage estimation based on the correlation between damage and surface morphology, such as cracks. However, image-based analytical estimation by physical modeling is extremely difficult because of the complexity of the morphology. Therefore, the proposed method is based on image recognition using deep learning. In particular, VGG19-based transfer learning was implemented. An L2-constrained softmax loss developed for face recognition was used, as the diversity of textures in the microscopic images was lower than that used for general object detection. As a result, the proposed method was able to accurately estimate the fatigue damage at the micro scale, which was at the submillimeter scale. The average estimation error was reduced to 15% of that obtained using the binarization method, indicating the L2-constrained softmax loss method to be highly effective. Although verification under a broad range of fatigue conditions is required for a more general evaluation, it was concluded that the proposed method is effective for evaluating the damage of thin metal films of flexible printed circuits at the micro scale.