Multiscale Texture Research Articles

Fusion of Multi-scale Features Seal End Face Fluid Characterization

The composite weaving of dynamic and static ring friction end faces with multi-scale features can improve the sealing performance of mechanical seals, but there is no clear mechanism of weaving action, and it is difficult to optimize seal surface weaving. In this study, a mathematical model is developed to analyze the fluid sealing performance of the composite textured mechanical seal end face with a combination of a 5-sided micro-pit and T-slot according to the mass conservation cavitation boundary condition. The change in sealing performance between the composite texture and single texture in terms of opening force and leakage volume was investigated. Numerical analysis shows that compared with a single texture, the multi-scale texture end face creates a coupling effect between the micro-pit and T-slot, which has a stronger dynamic pressure lubrication effect and improves the sealing performance of the sealing sub-seal with increased pressure. The research results can be applied to the design optimization of mechanical seal surface textures.

Deep endpoints focusing network under geometric constraints for end-to-end biometric measurement in fetal ultrasound images

Biometric measurements in fetal ultrasound images are one of the most highly demanding medical image analysis tasks that can directly contribute to diagnosing fetal diseases. However, the natural high-speckle noise and shadows in ultrasound data present big challenges for automatic biometric measurement. Almost all the existing dominant automatic methods are two-stage models, where the key anatomical structures are segmented first and then measured, thus bringing segmentation and fitting errors. What is worse, the results of the second-stage fitting are completely dependent on the good performance of first-stage segmentation, i.e., the segmentation error will lead to a larger fitting error. To this end, we propose a novel end-to-end biometric measurement network, abbreviated as E2EBM-Net, that directly fits the measurement parameters. E2EBM-Net includes a cross-level feature fusion module to extract multi-scale texture information, a hard-soft attention module to improve position sensitivity, and center-focused detectors jointly to achieve accurate localizing and regressing of the measurement endpoints, as well as a loss function with geometric cues to enhance the correlations. To our knowledge, this is the first AI-based application to address the biometric measurement of irregular anatomical structures in fetal ultrasound images with an end-to-end approach. Experiment results showed that E2EBM-Net outperformed the existing methods and achieved the state-of-the-art performance.

Combined effect of cavitation and multi-scale texture on the bearing properties of seawater-lubricated friction pair

To explore the tribological properties of a seawater-lubricated friction pair, the influence of cavitation and multi-scale texture on the load-carrying capacity is investigated under seawater lubrication conditions. In this paper, a numerical simulation method is proposed to reveal the influence mechanism of cavitation on the load-carrying capacity, and the simulation results are verified by experiments. The results show that cavitation, wedge action, and the vortex flow effect are the main mechanisms responsible for affecting the load-carrying capacity. Cavitation is not always advantageous for the load-carrying capacity. The results indicate that cavitation enhances the load-carrying capacity when the velocity is between 5 and 10 m/s. However, the cavitation effect shows a negative effect with a velocity exceeding 10 m/s. In three different scales of surface textures, the load-carrying capacity of the millimeter-scale texture is the highest, followed by the cross-scale texture and the micrometer-scale texture. The load-carrying capacity of the millimeter-scale texture is almost 10 times that of the micrometer-scale texture. Furthermore, regardless of the texture scale, a texture with a high depth-to-diameter ratio (H = 1) always exhibits a higher load-carrying capacity at low speeds (V < 10 m/s).

Image steganalysis method based on cover selection and adaptive filtered residual network

Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost.

Multiscale analyse of the relation between skid resistance and pavements surfaces texture evolution with polishing

It is known from the literature that skid resistance is closely related to road surface roughness. This paper aims to develop a multiscale texture decomposition method able to identify the relevant scales that affect pavement surfaces’ skid resistance variation during polishing. This method is based on continuous wavelets decomposition and. It is applied on seven pavement surfaces before and after polishing. The polishing is done in the laboratory using the Wehner & Schulze apparatus, which also serves for skid esistance measurements. At each step of polishing, 3D topographical maps of the test surface are realised with an optical sensor. On these maps, multiscale texture parameters are calculated after the multiscale decomposition. Analysis of these parameters shows their ability to represent the increase and the rate of increase in skid resistance in early age (increase in multiscale parameters for scale range 300–3000 µm) of pavement surfaces and the long-time skid resistance decrease (decrease in multiscale parameters for scale range 0–1000 µm.

Contribution of multiscale analysis to the understanding of friction evolution of aggregates surfaces

In this paper, a multiscale texture analysis method is developped and applied to aggregates surfaces to better explain the variation of road friction during polishing process. This method is based on continuous wavelets decomposition. The study is done on five aggregates types by varying aggregates hardness and mineralogical composition. Polishing tests and friction measurement are performed on each formula using Wehner & Schulze appartus to follow the evolution of friction with polishing. At each polishing stage, 3D mapping are made using Alicona Infinite Focus Sensor to follow the evolution of the surface texture. On these cartographies, multiscale texture parameters are calculated and analysed. Correlations between theses parameters and friction show that scales ranging between 0 and 1000 μm seem to be the most relevant to explain friction evolution.

Semantic prior-driven fused contextual transformation network for image inpainting

Recent advances in image inpainting have achieved impressive performance for generating plausible visual details on small regular image defects or simple backgrounds. However, current solution suffers from the lack of semantic priors for the image and the inability to deduce the image content from distant background, leading to distorted structures and artifacts in the results when inpainting large random irregular complicated images. To address these problems, a semantic prior-driven fused contextual transformation network for image inpainting is proposed as a promise solution. First, the semantic prior generator is put forward to map the semantic features of ground truth images and the low-level features of broken images to semantic priors. Subsequently, an image split-transform-aggregated strategy, named fusion context transformation block, is presented to infer rich multi-scale remote texture features and thus to improve the restored image finesse. Thereafter, an aggregated semantic attention-aware module, consisting of spatially adaptive normalization and enhanced spatial attention is designed to aggregate semantic priors and multi-scale texture features into the decoder to restore reasonable structure. Finally, the mask guided discriminator is developed to effectively discriminate between real and false pixels in the output image to improve the capability of the discriminator and hence to reduce the probability of artifacts containing in the output image. Comprehensive experimental results on CelebA-HQ, Paris Street View, and Places2 datasets demonstrate the superiority of the proposed network over the state-of-the-arts, whose PSNR, SSIM and MAE are improved about 20 %, 12.6 %, and 42 % gains, respectively.

Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques

Abstract Content Based Image Retrieval is ever growing technology for many applications such as medical, remote sensing, social media search engines and surveillance monitoring, etc,. Representing image content with appropriate features is tedious task using traditional low level feature extraction methods. Deep learning models achieved high precision in classification and object detection algorithms by extracting automated high level feature extraction process. This paper proposed a hybrid feature extraction technique by combining the high level features and low level features to improve the robustness of the feature vector. The proposed model used pre-trained Googlenet model as feature extractor and combined with Gabor multiscale texture features. The final feature vector will be used for retrieving the relevant image data from the large scale image dataset. It has achieved the precision of 91 percent which shows better than state of art methods.

Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study

BackgroundTumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed.MethodsIn this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V1, n = 115; V2, n = 116; and V3, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways.ResultsA set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72–16.44; P = 0.0037) and the three external validation sets (V1: HR 2.63, 95%CI 1.10–6.29, P = 0.0292; V2: HR 2.99, 95%CI 1.34–6.66, P = 0.0075; V3: HR 1.93, 95%CI 1.15–3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V1: 0.704 vs. 0.679; V2: 0.728 vs. 0.666; V3: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent.ConclusionsMPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.

Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications.

Scale-Free Distribution of Oxygen Interstitial Wires in Optimum-Doped HgBa2CuO4+y

Novel nanoscale probes are opening new venues for understanding unconventional electronic and magnetic functionalities driven by multiscale lattice complexity in doped high-temperature superconducting perovskites. In this work, we focus on the multiscale texture at supramolecular level of oxygen interstitial (O-i) atomic stripes in HgBa2CuO4+y at optimal doping for the highest superconducting critical temperature (TC) of 94 K. We report compelling evidence for the nematic phase of oxygen interstitial O-i atomic wires with fractal-like spatial distribution over multiple scales using scanning micro- and nano-X-ray diffraction. The scale-free distribution of O-i atomic wires at optimum doping extending from the micron down to the nanoscale has been associated with the intricate filamentary network of hole-rich metallic wires in the CuO2 plane. The observed critical opalescence provides evidence for the proximity to a critical point that controls the emergence of high-temperature superconductivity at optimum doping.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy

PurposeOnline adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART. MethodsA machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve. ResultsSpearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2–3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98. ConclusionsA machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.

Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification.

Infrared and visible light dual-camera super-resolution imaging with texture transfer network

As low-resolution (LR) images often miss a large amount of feature information, the super-resolution (SR) algorithm that directly maps and reconstructs a single LR infrared image faces a considerable challenge due to the lack of prior features. We propose an infrared and visible light dual-camera SR imaging scheme based on a visible light texture transfer (VTTISR) method, which fills in the missing feature information of LR infrared images by transforming visible light texture features. The proposed VTTISR method consists of a multi-scale texture progressive transfer network and a cross-scale residual aggregation reconstruction network. The multi-scale texture progressive transfer network migrates visible light texture features to infrared features to generate multi-scale texture transfer features. The cross-scale residual aggregation reconstruction network sequentially fuses texture features at corresponding scales and generates 4-magnification SR infrared image. Experimental results show that VTTISR has made significant progress in quantitative image evaluation with higher efficiency.

Multi-Scale Semi-Coupled Convolutional Sparse Coding for the Super-Resolution Reconstruction of Remote Sensing Image

The traditional convolutional sparse coding super-resolution method only introduces the linear projection relationship in the feature space conversion and fails to consider the local detail in the feature map learning, which causes the reconstruction results to be unsatisfactory in terms of edges and details. The convolutional sparse coding theory is introduced into the super-resolution reconstruction framework of remote sensing images and a multi-scale semi-coupled convolutional sparse coding super-resolution reconstruction method is proposed. Firstly, the input image is decomposed by multi-scale to extract smooth components and multi-scale texture components, and the final smoothing components are reconstructed by bicubic interpolation. Then, the texture components of each scale are reconstructed by semi-coupled convolution sparse coding. The nonlinear convolution operator is used as the projection function between the high-resolution feature map and the low-resolution feature map of texture component at each scale and the non-local self-similarity structure in the feature map learning for constrained optimization is introduced to better reconstruct the texture image at each scale. Finally, the reconstructed smooth component and the texture component at each scale are superimposed to obtain the final reconstructed image. Remote sensing images from 4 different sensors are used as experimental images and the state-of-the-art super-resolution reconstruction methods are compared. Experimental results show that the reconstructed images obtained by the proposed method are better than other methods in quantitative analysis index PSNR and FSIM and show clearer boundary and detailed information and have a certain anti-noise performance.

Single-Image Super-Resolution Neural Network via Hybrid Multi-Scale Features

In this paper, we propose an end-to-end single-image super-resolution neural network by leveraging hybrid multi-scale features of images. Different from most existing convolutional neural network (CNN) based solutions, our proposed network depends on the observation that image features extracted by CNN contain hybrid multi-scale features: both multi-scale local texture features and global structural features. By effectively exploiting these multi-scale and local-global features, our network involves far fewer parameters, leading to a large decrease in memory usage and computation during inference. Our network benefits from three key modules: (1) an efficient and lightweight feature extraction module (EFblock); (2) a hybrid multi-scale feature enhancement module (HMblock); and (3) a reconstruction–restoration module (DRblock). Experiments on five popular benchmarks demonstrate that our super-resolution approach achieves better performance with fewer parameters and less memory consumption, compared to more than 20 SOTAs. In summary, we propose a novel multi-scale super-resolution neural network (HMSF), which is more lightweight, has fewer parameters, and requires less execution time, but has better performance than the state-of-the-art methods. Compared to SOTAs, this method is more practical and better suited to run on constrained devices, such as PCs and mobile devices, without the need for a high-performance server.

Investigation of novel multiscale textures for the enhancement of the cutting performance of Al2O3/TiC ceramic cutting tools

Currently, the high temperature and severe friction conditions at the tool-chip interface are the main reasons for ceramic tool wear failures. Surface texturing as a geometric extension for cutting tools is a promising way to extend their service life. In this study, a novel type of multiscale texture was developed, and its effect on the cutting performance of an Al2O3/TiC ceramic cutting tool while machining AISI H13 steel was explored in a conventional cooling environment. The cutting force, cutting temperature, and tool wear morphology were investigated at cutting velocities ranging from 80 to 249 m/min. Microgroove textured Al2O3/TiC ceramic tools were prepared for comparison. The results show that the structure of the multiscale textures maintained good integrity over the range of cutting velocities. Thus, the synergistic effect of the microscale and nanoscale textures promoted the introduction and permeation of the cutting fluid. Therefore, the multiscale textures effectively enhanced the cutting performance of the Al2O3/TiC ceramic tools.

An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography.

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration.

Multiscale Structure and Texture Feature Fusion for Image Inpainting

In order to achieve interaction between structure and texture information in generative adversarial image inpainting networks and improve the semantic veracity of the restored images, unlike the original two-stage inpainting ideas where texture and structure are restored separately, this paper constructs a multi-scale fusion approach to image generation, which embeds images into two collaborative subtasks, that is, structure generation and texture synthesis under structural constraints. We also introduce a self-attention mechanism into the partial convolution of the encoder to enhance the long range contextual information acquisition of the model in image inpainting, and design a multi-scale fusion network to fuse the generated structure and texture feature, so that the structure and texture information can be reused for reconstruction, perception and style loss compensation, thus enabling the fused images to achieve global consistency. In the training phase, feature matching loss are introduced to enhance the image in terms of structural generation plausibility. Finally, through comparison experiments with other inpainting networks on the CelebA, Paris StreetView and Places2 datasets, it is demonstrated that our method constructed in this paper has better objective evaluation metrics, more effective inpainting of structural and texture information of corrupted images and better image inpainting performance.

D2BGAN: A Dark to Bright Image Conversion Model for Quality Enhancement and Analysis Tasks Without Paired Supervision

This paper presents an image enhancement model, D2BGAN (Dark to Bright Generative Adversarial Network), to translate low light images to bright images without a paired supervision. We introduce the use of geometric and lighting consistency along with a contextual loss criterion. These when combined with multiscale color, texture and edge discriminators prove to provide competitive results. We performed extensive experiments using benchmark datasets to visually and objectively compare our results. We observed the performance of D2BGAN on real-time driving datasets that are subject to motion blur, noise, and other artifacts. We further demonstrated that our enhanced images can be profitably used in image-understanding tasks. Images processed using our technique obtain the best or second best average scores for three different image quality evaluation methods on the Naturalness Preserved Enhancement (NPE), Low Light Image Enhancement (LIME), Multi-Exposure Image Fusion (MEF) benchmark datasets. Best scores are also obtained on the LOw-Light (LOL) test set and on Berkeley Driving Dataset (BDD) images processed with D2BGAN. Face detection tasks on the DarkFace benchmark dataset show an mAP (mean Average Precision) improvement from 0.209 to 0.301 when images are processed using D2BGAN. mAP further improves to 0.525 when finetuning techniques are adopted.