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Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image. [ABSTRACT FROM AUTHOR]

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the lowfrequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%. [ABSTRACT FROM AUTHOR]

This paper proposes a local texture feature descriptor which fuses the center pixel information into the Center-Symmetric Local Binary Pattern (CS-LBP) for the purpose of face recognition. Because of its tolerance to illumination changes, and computational efficiency, the CS-LBP is widely used in face recognition. But this operator completely ignores the center pixel information which may affect the discriminative result in some applications. In order to take advantage of more useful information, this paper fuses the center pixel information into CS-LBP descriptor, namely CS-LBP/Center. In face recognition, the face image is first divided into small blocks from which CS-LBP/Center histograms are extracted and then weighted by image entropy. Finally, all the weighted histograms are connected serially to create a final texture descriptor for face recognition. The experimental results on some face datasets show that a higher recognition accuracy can be obtained by employing the proposed method with nearest neighbor classification. [ABSTRACT FROM AUTHOR]

Green plant species identification plays an important role in so many aspects, such as ecological environment protection, Chinese medicine preparation, agricultural and horticultural application, etc. A method on green plants recognition based on wavelet transform and variable local edge patterns (VLEP) is proposed in this paper. Firstly, the original image is decomposed by wavelet transformation. Then texture features are extracted using VLEPs. At the same time, block-based and multi-resolution ideas are considered together to extract features after images are transformed by wavelet. Finally, the fused texture features are classified by the nearest neighbor method. The experimental results show that the proposed method is a promising method for recognizing the common green plants with the natural and complex background compared with the other state-of-the-art methods, and combination of block-based and multi-resolution ideas can further improve the accuracy rate effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a weighted large margin nearest center (WLMNC) distance-based human depth recovery method for tele-immersive video interaction systems with limited bandwidth consumption. In the remote stage, the proposed method highly compresses the depth data of the remote human into skeletal block structures by learning the WLMNC distance, which is equivalent to downsampling the human depth map at $64{\times}$ the sampling rate. In the local stage, the method first recovers a rough human depth map based on a WLMNC distance augmented clustering approach and then obtains a fine depth map based on a rough depth-guided autoregressive model to preserve the depth discontinuities and suppress texture copy artifacts. The proposed WLMNC distance is learned by the large margin clustering problem with a weighted hinge loss to balance the clustering accuracy and depth recovery accuracy and is verified to be able to preserve depth discontinuities between skeletal block structures with occlusion. A theoretical analysis is conducted to verify the effectiveness of using the weighted hinge loss. Furthermore, a novel data set containing various types of human postures with self-occlusion is built to benchmark the human depth recovery methods. The quantitative comparison with the state-of-the-art depth recovery methods on the introduced benchmark data set demonstrates the effectiveness of the proposed method for human depth recovery with such a high upsampling rate. [ABSTRACT FROM AUTHOR]

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the influence of dimple textures on the friction and wear properties of tapered roller bearings (TRBs) with many pattern parameters, e.g. diameter, depth and area density under starved lubrication. Design/methodology/approach: The pattern parameters include the dimple diameter (D; 60, 100 and 200 µm), dimple depth (H; 5, 10 and 20 µm) and area density (S; 6%, 12% and 24%). Dimples were fabricated on the outer ring (OR) of TRBs using a laser marking machine. The tribological properties of dimple-textured TRBs under starved lubrication were studied on a vertical universal friction wear tester with special friction pairs. The effect mechanisms of dimple textures on the tribological properties of TRBs are presented and summarized through experiments and discussions. Findings: When dimple-textured TRBs revolve under starved lubrication, the average coefficients of friction (ACOF) and wear losses are markedly lower than those of non-textured bearings. S has the greatest influence on the COF curve. When D is 100 µm and S is 24%, the ACOF and wear losses are both lowest, i.e. 0.00426 and 0.51 mg, respectively. Under the same test conditions, compared with the non-textured group, its COF and wear loss decreased by 35.6% and 62.5%, respectively. Originality/value: This work provides a useful reference for the research on the raceways of textured TRBs. [ABSTRACT FROM AUTHOR]

This paper presents a study on the effectiveness of texture analysis of remote sensing imagery depending on the type and spatial resolution of the source image. The study used the following image types: near-infrared band, red band, first principal component, second principal component and normalized difference vegetation index images of pixel size from 2 m to 30 m, generated from a multispectral WorldView-2 image. The study evaluated the separability of the selected pairs of the following land cover classes: bare soil, low vegetation, coniferous forest, deciduous forest, water reservoirs, built-up areas. The tool used for texture analysis was granulometric analysis based on morphological operations—one of less popular methods which, however, as demonstrated by previous studies, shows high effectiveness in separating classes of different texture. The conducted study enabled researchers to evaluate the significance of image type and resolution for visibility of texture in the image and the possibility of using texture to differentiate between classes. The obtained results showed that there is no single, universal combination of conditions of texture analysis, which would be the best from the point of view of all classes. For most of the analyzed pairs of classes, the best results were obtained for the highest spatial resolution of the image (2–3 m), but the class of built-up areas stands out in this comparison—the best distinction was obtained with the average spatial resolution (10–15 m). Research has also shown that there is no single type of image that is universally the best basis for texture analysis. While for the majority of classes the image of the first principal component was the best, for the class of built-up areas it was the image of the red channel. [ABSTRACT FROM AUTHOR]

Extensive network receptive field is key for unsupervised affine registration because instead of deformable registration that takes care of local subtleties, the affine registration is global so that the last layers need to see big patches of the organ‐in‐interest. To extend the network's receptive field, we need to go for deeper networks, which causes producing complex models. On the other hand, affine transformation is restricted by its low degree‐of‐freedom (DoF) where larger models increasingly develop the hazard of overfitting. To worsen the situation, the regularizer module cannot be applied to the affine transformation with such a restricted DoF. In this paper, we propose a differentiable computational layer to convert the affine transformation outputted by the network to its corresponding dense displacement field. Such an affine‐to‐field layer enables us to apply different regularization terms on the outputted transformation in order to avoid the overfitting phenomenon while deepening the network. The proposed approach was evaluated on an annotated hard multimodal dataset containing 1109 pairs of CT/MR images of the brain with different heterogeneity for example, variety in scanners, setups and resolutions. Based on the results, the proposed customized layer is fully successful to handle the overfitting for deeper networks that are able to produce richer transformations than the shallower networks from different evaluation metrics for example, in target registration error the proposed network with seven layers has a 13.3% (or 9.1 mm) improvement in performance. The implementation of the proposed customized affine‐to‐field layer in the Python, Keras package with the Tensorflow backend can be publically accessed via https://github.com/boveiri/Deep-coReg. [ABSTRACT FROM AUTHOR]

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of discovering a Petri Net (PN) from a long event sequence representing the behavior of discrete-event processes. A method for building a 1-bounded PN able to execute the events sequence $S$ is presented; it is based on determining causality and concurrence relations between events and computing the t-invariants. This novel method determines the structure and the initial marking of an ordinary PN, which reproduces the behavior in $S$. The algorithms derived from the method are efficient and have been implemented and tested on numerous examples of diverse complexity. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) retrieves images from image database based on the visual similarity of query image. For the implementation of CBIR, feature extraction plays a significant role, where colour feature is quite remarkable. But, due to achromatic surfaces or unevenly colored, the role of texture is also important. This paper introduced an efficient and fast CBIR system, which is based on the combination of computationally light weighted colour and texture features viz. chromaticity moment, colour percentile, and local binary pattern. For searching, this paper proposes inverse variance based varying weighted similarity measure (low for high variance feature and high for low variance feature), which reduces the effect of redundancy by assigning the priority to each feature, and effectively retrieves relevant images. In addition, this paper also proposes query image classification and retrieval model by filtering out irrelevant class images using Random Forests (RF) classifier, which recovers the class of a query image based on distinct learning (supervised) of various decision trees. This successful ensemble classification of query images reduces the semantic gap, searching space, and enhances the retrieval performance. Extensive experimental analyses on benchmark databases confirm the usefulness and effectiveness of this work. [ABSTRACT FROM AUTHOR]

Virtual point lights (VPLs) are well established for real-time global illumination. However, this method suffers from spiky artifacts and flickering caused by singularities of VPLs, highly glossy materials, high-frequency textures, and discontinuous geometries. To avoid these artifacts, this paper introduces a virtual spherical Gaussian light (VSGL) which roughly represents a set of VPLs. For a VSGL, the total radiant intensity and positional distribution of VPLs are approximated using spherical Gaussians and a Gaussian distribution, respectively. Since this approximation can be computed using summations of VPL parameters, VSGLs can be dynamically generated using mipmapped reflective shadow maps. Our VSGL generation is simple and independent from any scene geometries. In addition, reflected radiance for a VSGL is calculated using an analytic formula. Hence, we are able to render one-bounce glossy interreflections at real-time frame rates with smaller artifacts. [ABSTRACT FROM AUTHOR]

A study on using an unsupervised learning technique, called a self-organizing map (SOM) or Kohonen map, for the detection of road cracks from pavement images is described in this paper. The main focus is on highly textured road images that make the crack detection very difficult. Road images are split into smaller rectangular cells, and a representative data set is generated for each cell by analyzing image texture and color properties. Texture and color properties are combined with a Kohonen map to distinguish crack areas from the background. Using this technique, cracks are detected to a precision of 77%. The algorithm also resulted in a recall of 73% despite the background having very strong visual texture. The technique applied here shows a great deal of promise despite the images being captured in an uncontrolled environment devoid of state-of-the-art image-acquisition setups. The results are also benchmarked against an advanced algorithm reported in a recent research paper. The benchmarking shows that the proposed algorithm performs better in terms of reducing the false positives in crack detection. [ABSTRACT FROM AUTHOR]

Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channel which may cause severe artifacts in the reconstructed images. Furthermore, since the images in different energy channels describe the same object, there are high correlations among different channels. To make full use of the inter-channel correlations and minimize the count starving effect while maintaining clinically meaningful texture information, this paper combines a region-specific texture model with a low-rank correlation descriptor as an a priori regularization to explore a superior texture preserving Bayesian reconstruction of spectral CT. Specifically, the inter-channel correlations are characterized by the low-rank representation, and the inner-channel regional textures are modeled by a texture preserving Markov random field. In other words, this paper integrates the spectral and spatial information into a unified Bayesian reconstruction framework. The widely-used Split-Bregman algorithm is employed to minimize the objective function because of the non-differentiable property of the low-rank representation. To evaluate the tissue texture preserving performance of the proposed method for each channel, three references are built for comparison: one is the traditional CT image from energy integration detection. The second one is spectral images from dual-energy CT. The third one is individual channels images from custom-made photon-counting spectral CT. As expected, the proposed method produced promising results in terms of not only preserving texture features but also suppressing image noise in each channel, comparing to existing methods of total variation (TV), low-rank TV and tensor dictionary learning, by both visual inspection and quantitative indexes of root mean square error, peak signal to noise ratio, structural similarity and feature similarity. [ABSTRACT FROM AUTHOR]

Infrared target intrusion detection has significant applications in the fields of military defence and intelligent warning. In view of the characteristics of intrusion targets as well as inspection difficulties, an infrared target intrusion detection algorithm based on feature fusion and enhancement was proposed. This algorithm combines static target mode analysis and dynamic multi-frame correlation detection to extract infrared target features at different levels. Among them, LBP texture analysis can be used to effectively identify the posterior feature patterns which have been contained in the target library, while motion frame difference method can detect the moving regions of the image, improve the integrity of target regions such as camouflage, sheltering and deformation. In order to integrate the advantages of the two methods, the enhanced convolutional neural network was designed and the feature images obtained by the two methods were fused and enhanced. The enhancement module of the network strengthened and screened the targets, and realized the background suppression of infrared images. Based on the experiments, the effect of the proposed method and the comparison method on the background suppression and detection performance was evaluated, and the results showed that the SCRG and BSF values of the method in this paper had a better performance in multiple data sets, and it's detection performance was far better than the comparison algorithm. The experiment results indicated that, compared with traditional infrared target detection methods, the proposed method could detect the infrared invasion target more accurately, and suppress the background noise more effectively. [ABSTRACT FROM AUTHOR]

The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, k -nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with k -nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study a new liquid-phase assisted texture treatment method to improve the tribological properties of 304 stainless steel. Design/methodology/approach: Three groups of textured type (KY, KJ and YJ) were prepared on 304 stainless steel surface using laser circular and cross scanning method in air and liquid assisted condition. The surface morphology and element content of test samples were measured with scanning electron microscope, energy spectrum. Then, the tribological test was carried out using MWF-500 reciprocating friction and wear testing machine under dry and oil lubrication condition. Findings: The experimental results showed that the textured surface of laser processing in air was obviously blackened, and the oxygen content was increased from 16.9% to 24%. These cases did not occur on liquid-assisted laser textured surface, which induced a better wettability and surface texture processing quality. For friction test, the friction coefficient of cross-scanning textured surface prepared in assisted liquid (YJ) was the smallest. It is reduced by 55% in oil lubrication case compared to the original surface (YS). The cross-scanning textured surface prepared in air (KJ) was a little worse in friction coefficient and a little better in wear quantity than the cross-scanning textured surface prepared in assisted liquid (YJ). It is indicated that the laser processing surface with assisted liquid has obvious advantages in surface texture quality and interfacial tribological property. The main reason is that the assisted liquid plays a role in cooling and protecting action of the machined surface. The bubbles, generated at the solid–liquid interface because of the laser heat effect, scatter the laser beam and carry out the processed melt meanwhile. The lubricating medium is easier to penetrate and store in the contact interfaces because of the higher surface textured performance and wettability. Originality/value: The main contribution of this work is in providing a new surface texture processing method that has a better surface micropits quality and interfacial tribology regulation ability. [ABSTRACT FROM AUTHOR]

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values. [ABSTRACT FROM AUTHOR]

The paper presents certain problems related to the lubrication of textured surfaces. It was noted that for a specific load, the lubrication effectiveness will depend on the shape, size and mutual distribution of the texture's elements. Proposals for determining the lubrication irregularity parameters were presented for textures with a rectangular mesh and round recesses. The experimental part presents the laser texturing methodology, tribological testing methodology and preliminary test results where the significant role of the relationship between the trajectory of motion and the texture mesh was demonstrated. [ABSTRACT FROM AUTHOR]

Video smoke detection benefits life safety and environment protection. A method of video smoke detection using shape, color and dynamic texture features is presented in this paper. Firstly, an algorithm identifying cone geometry feature is used to distinguish conical region from dynamic regions. Secondly, conical regions are filtered by a color filtering algorithm to further test the candidate smoke region. Finally, a texture filtering algorithm is used to differentiate true smoke from candidate smoke regions. Experiments show that the proposed method is effective and it results in earlier and more reliable detection than the other two methods reported in the literature. The processing rate of the smoke detection method achieves 25 frames per second with an image size of 320×240 pixels. [ABSTRACT FROM AUTHOR]

Skin cancer is a most dangerous type of cancer found in humans. It is found in various types such as melanoma, basal cell carcinoma and squamous cell carcinoma. Among others, Melanoma is the most serious and dangerous cancer which leads from a simple skin mark to a tumour. Early detection of the type of skin cancer can helpsin better cure. In this paper, a new method is proposed for the detection of type of skin cancer. The proposed method integrates the features of DRLBP (Dominant rotated local binary pattern) and MRELBP (median robust extended local binary pattern) methods for feature extraction of the skin cancer lesions. The support vector machine (SVM) classifier is then used to classify the images based on the calculated features. To evaluate the performance of the proposed method, skin cancer images containing 367 lesions are used from ISICstandard dataset from the results, it is analysed that the proposed method can extracts microstructure and macrostructure texture information. Furthermore, it is robust to the rotation variation.It is also observed that the proposed method gives better results than the other state of the art local binary pattern based feature extraction methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of multi-dimensional dynamic texture analysis, and we introduce a new higher order linear dynamical system (h-LDS) descriptor. The proposed h-LDS descriptor is based on the higher order decomposition of the multidimensional image data and enables the analysis of dynamic textures by using information from various image elements. In addition, we propose a methodology for its application to video-based early warning systems that focus on smoke identification. More specifically, the proposed methodology enables the representation of video subsequences as histograms of h-LDS descriptors produced by the smoke candidate image patches in each subsequence. Finally, to further improve the classification accuracy, we propose the combination of multidimensional dynamic texture analysis with the spatiotemporal modeling of smoke by using a particle swarm optimization approach. The ability of the h-LDS to analyze the dynamic texture information is evaluated through a multivariate comparison against the standard LDS descriptor. The experimental results that use two video datasets have shown the great potential of the proposed smoke detection method. [ABSTRACT FROM PUBLISHER]

The study of phenomena segmentation in natural scenes has attracted growing attention and is a popular research topic. While there are many studies detailing algorithms for motion segmentation in dynamic scenes, an important question arising from these studies is how to combine these algorithms. How can the label correspondence problem be resolved? Answering this question is difficult, because there are no labeled training data available in clustering to guide the search. Also, different algorithms produce incompatible data labels resulting in intractable correspondence problems. This paper presents a new consensus model for motion segmentation in dynamic scenes, which aims to combine several unsupervised methods to achieve a more reliable and accurate result. The advantage of our method is that it is intuitively appealing. Numerical experiments on various phenomena are conducted. The performance of the proposed model is compared with the best state-of-the-art motion segmentation methods recently proposed in the literature, demonstrating the robustness and accuracy of our method. [ABSTRACT FROM AUTHOR]

In recent years, the problem of how to more perceptually quantify visualizations of an object and a surface displayed in 3D space between the human eye and a display has been more prominent with rapid increase in the demand for 3D/ultrahigh-definition content. In order to quantify the content information in terms of human visual perception, it is necessary to measure the visual information of 2D and 3D videos accurately in accordance with the human visual system. In this paper, we investigate a new framework for expressing visual information in bits termed visual entropy, based on information theory. 2D visual entropy (2DVE) is composed of two major components: 1) texture entropy on the 2D surface and 2) depth entropy based on the monocular cue. In contrast, 3D visual entropy (3DVE) includes the depth entropy based on the binocular cue in addition to the 2DVE. A series of simulations is conducted to demonstrate the effectiveness of visual entropy, including the degree of accuracy needed to represent perceptual information by means of the texture and monocular and binocular depth entropies. In the simulation results, two successful example applications are also provided, whereby visual entropy is applied to the problems of predicting the visual discomfort experienced when viewing 3D displays and of analyzing performance tradeoffs between 2D and 3D contents. [ABSTRACT FROM AUTHOR]

Digital Image Processing is the process to perform various types of operations on different types of images. Content based image retrieval is branch of digital image processing. CBIR works in the principle of contents. In CBIR collared, medical and gray scale images have been used for processing. In medical images different types of content shapes have been extracted from the query image and image that contain particular shapes or contents have been extracted from database images. We studied various papers on CBIR. The main problem in the CBIR for medical images is that these shape or content features does not provide better results. In our work we will remove this by using various features like angular movement of image, correlation, entropy and energy of the image also has been computed. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present an analysis of several 3 D texture parameters for the entire wear scars obtained in severe regime, on a four-ball tester. The aim of this analysis is to correlate the tribological parameter as wear scar diameter to texture parameters. Design/methodology/approach: Tested lubricants were rapeseed oil, rapeseed oil additivated with 1% Wt nano TiO2 and rapeseed oil additivated with 1%Wt nano ZnO. The severe regime was applied for 1400 rpm and for loads increasing in steps of 50 N, from 500 to 900 N. Several analyzed roughness parameters (height parameters and functional ones) could be related to the evolution of a wear parameter, the wear scar diameter. Comparing the values for neat rapeseed oil and additivated variants, the texture parameters allow for evaluating if the additives protect or not the worn surfaces. Findings: Measurements pointed out two groups of roughness parameters: one that has an evolution depending on wear scar diameter (WSD) and load (Sa, St, functional parameters) and one including Ssk that has shown no dependence on load and WSD. Also, the functional parameters Spk and Svk follow in a similar manner the wear parameter, WSD, but Sk is the least dependent on load. For the highest load, amplitude parameters such as Sa and St are following the tendency of WSD. Each lubricant has its particular correlation between wear parameters and texture quality, expressed by the help of a set of roughness parameters. Research limitations/implications: Such studies help tribologists to rank lubricants based on a combined analysis with wear parameters and texture parameters. Practical implications: The results allow for evaluating new formulated lubricants. Originality/value: The study on the quality on worn surfaces introduces the original idea of analyzing the entire wear scar surface (approximated by an ellipse with the axes as those experimentally measured) by the help of a set of 3 D roughness parameters. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the influence of sinusoidal texture (ST) with different morphology parameters on the corresponding tribological effects. Design/methodology/approach: The STs with different amplitudes, ranging from 0.05 to 0.3 mm, and frequencies, ranging from 5 to 17.5, are fabricated using nanosecond pulsed laser equipment. The friction experiments and the finite element analysis method are combined to investigate the tribological properties, under dry friction conditions. Findings: Test results show that when the amplitude is 0.15 mm and frequency is 10, ST surface has the lowest friction coefficient of 0.373, and exhibits great anti-friction effect. It also possesses a complete texture edge after friction. The friction reduction effect of ST with larger or smaller amplitude and frequency is worse. Originality/value: The results of this study can provide a guidance for the design optimization of ST of reciprocating sliding contact surfaces, under dry friction conditions. [ABSTRACT FROM AUTHOR]

Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient’s previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients’ FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans. [ABSTRACT FROM AUTHOR]

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algorithm to combine outputs of GANs trained on a smaller resolution to produce a large-scale plausible texture map with virtually no boundary artifacts. Second, we propose a user interface to enable artistic control. Our quantitative and qualitative results showcase the generation of synthesized high-resolution maps consisting of up to hundreds of megapixels as a case in point. [ABSTRACT FROM AUTHOR]

The High Efficiency Video Coding (HEVC) adopts 35 intra prediction modes to provide a more precise intra prediction. As a result, HEVC intra coding is much more complex. To provide a flexible solution for battery-powered devices, which include varying power budgets based on battery level, this paper jointly employs statistical distribution of different intra modes, and texture analysis. While the statistical information specifies the most efficient modes for extremely low power budgets, for moderate to high power budgets the best angular modes are predicted by analyzing the residual of the Planar mode, which is demonstrated to represent the dominant edges of the block. Moreover, a scheme is presented that further reduces the complexity by predicting the more important DC and Planar modes using the information provided by the entropy coder. The proposed method imposes almost no computational overhead since it utilizes the existing internal operators of HEVC. Ultimately, an intra prediction scheme with seven predefined levels of complexity is presented that provides coding for various power budgets and devices with various capabilities. The highest level of this scheme provides ~23% total encoding time reduction compared to HM, in all-intra configuration, with only 0.78% penalty on BD-Rate for high end devices. While the lowest level makes encoding possible for extremely low power devices and low battery plans with ~52% time reduction (almost the entire share of intra coding) at the cost of 9.6% BD-Rate, which is shown to be nearly minimal for this level of processing power. [ABSTRACT FROM AUTHOR]

Modelling flow phenomena and their related weathering effects is often cumbersome due their dependence on the environment, materials and geometric properties of objects in the scene. Example‐based modelling provides many advantages for reproducing real textures, but little effort has been devoted to reproducing and transferring complex phenomena. In order to produce realistic flow effects, it is possible to take advantage of the widespread availability of flow images on the Internet, which can be used to gather key information about the flow. In this paper, we present a technique that allows the transfer of flow phenomena between photographs, adapting the flow to the target image and giving the user flexibility and control through specifically tailored parameters. This is done through two types of control curves: a fitted theoretical curve to control the mass of deposited material, and an extended colour map for properly adapting to the target appearance. In addition, our method filters and warps the input flow in order to account for the geometric details of the target surface. This leads to a fast and intuitive approach to easily transfer phenomena between images, providing a set of simple and intuitive parameters to control the process. Modelling flow phenomena and their related weathering effects is often cumbersome due their dependence on the environment, materials, and geometric properties of objects in the scene. Example‐based modelling provides many advantages for reproducing real textures, but little effort has been devoted to reproducing and transferring complex phenomena. In order to produce realistic flow effects, it is possible to take advantage of the widespread availability of flow images on the Internet, which can be used to gather key information about the flow. In this paper, we present a technique that allows the transfer of flow phenomena between photographs, adapting the flow to the target image and giving the user flexibility and control through specifically tailored parameters. This is done through two types of control curves: a fitted theoretical curve to control the mass of deposited material, and an extended colour map for properly adapting to the target appearance. [ABSTRACT FROM AUTHOR]

In order to construct 3D meshes from a single image quickly and intuitively, this paper presents a 3D reconstruction method based on control point grid. The key idea is to calculate the 2D control point grid of the target area and elevate each control point according to a parameterized 3D growing rule proposed from prior knowledge. First, the contour of the target object is divided into a major component and side components and the skeleton of each component is extracted. 2D control point grid is calculated combining the curvature and geometric feature of the contour and lifted into 3D space with corresponding height defined by parameter mapping. Finally, a complete 3D model is obtained after component combination and texturing. Experimental results show that this method can reasonably and efficiently recover the 3D shape of the target object while maintaining fine curvature and geometric features. [ABSTRACT FROM AUTHOR]

In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors. Our Geo-Tex VAE model extends the convolutional variational autoencoder for face tracking, and jointly learns and represents deformations and variations in geometry and texture from tracked meshes and texture maps. To accurately model variations in facial geometry and texture, we introduce the decomposition layer in the Geo-Tex VAE architecture which decomposes the facial deformation into global and local components. We train the global deformation with a fully-connected network and the local deformations with convolutional layers. Despite running this model on each frame independently - thereby enabling a high amount of parallelization - we validate that our framework achieves sub-millimeter accuracy on synthetic data and outperforms existing methods. We also qualitatively demonstrate high-fidelity, long-duration facial performance tracking on several actors. [ABSTRACT FROM AUTHOR]

Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. [ABSTRACT FROM AUTHOR]

Face recognition in the reality, is a challenging problem, due to varieties in illumination, background, pose etc. Recently, the deep learning based face recognition algorithm is able to learn effective face features to obtain a very impressive performance. However, this kind of face recognition algorithm completely relies on the machine learning based face features, while ignores the useful experience in hand-craft features which have been studied in a long period. Therefore, a face recognition based on facial texture feature aided deep learning feature (FTFA-DLF) is proposed in this paper. The proposed FTFA-DLF is able to combine the benefits of deep learning and hand-craft features. In the proposed FTFA-DLF method, the hand-craft features are texture features extracted from the eyes, nose, and mouth regions. Then, the hand-craft features are used to aid deep learning features by adding both deep learning and hand-craft features into the objective function layer, which adaptively adjusts the deep learning features so that it can better cooperate with the hand-craft features and obtain a better face recognition performance. Experimental results show that the proposed face recognition algorithm on the LFW face database to achieve the accuracy rate of 97.02%. [ABSTRACT FROM AUTHOR]

Methodology for analysing textural properties of sugar beet roots in the laboratory has previously been established. It has been shown to be reliable and of value in exploring relationships between textural properties, damage rates, and storability of varieties. In this paper, a methodology for the assessment of textural properties in-field, prior to harvest, using an inexpensive handheld penetrometer is examined. Three sugar beet varieties were grown in Belgium, the Netherlands, and Sweden during 2019. Textural properties were assessed in-field with the handheld penetrometer 2, 1 and 0 months prior to harvest, and with the laboratory penetrometer directly after harvest. Comparison of the results showed generally strong correlations. A power analysis suggests a difference in mean Handheld Pressure of 0.10 MPa could be found significant within a large trial with a block design. The reliability of the handheld penetrometer was further assessed in the Swedish national variety trials over three years (2019-2021). Correlation coefficients of 0.86 and 0.94 were found between mean Handheld Pressure for 2019 and 2020, and 2020 and 2021 respectively. The handheld penetrometer can be applied as an economic means of quantifying differences in textural properties of sugar beet varieties. Clear operating procedure and training must exist. [ABSTRACT FROM AUTHOR]

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models. The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency, so it is widely used in close-range, aerial and satellite image matching. Based on the analysis of the original semi-global matching algorithm, this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture. The method includes 4 parts; (T) Precise orientation. Aiming at the system error in the image orientation model, the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images, and the sub-pixel positioning accuracy is obtained; (2) Epipolar image generation. After the original image is divided into blocks, the epipolar image is generated based on the projection trajectory method; (3) Image dense matching. In order to reduce the size of the cost space and calculation time, the image is pyramided and then semi-globally matched layer by layer. In the matching process, the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects; @ Generate DSM. In order to test the matching effect, the weighted median filter algorithm is used to filter the disparity map, and the DSM is obtained based on the principle of forward intersection. Finally, the paper uses the matching results of WordView3 and Ziyuan No. 3 stereo image to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper proposes a method of effectively segmenting text areas that exist in images by using the texture features of various types of input images obtained in social multimedia networks with an artificial neural network. The proposed text segmentation method consists of four main steps: a step for extracting candidate text areas, a step for localizing the text areas, a step for separating the text from the background, and a step for verifying the candidate text areas. In the candidate text area extraction step, candidate blocks that have any text areas are segmented in an input image on the basis of the texture features of the candidate blocks. In the text area localization step, only strings are extracted from the candidate text blocks. In the text and background separation step, the text areas are separated from the background area in the localized text blocks. In the candidate text area verification step, an artificial neural network is used to verify whether the extracted text blocks include actual text areas and exclude non-text areas. In the experimental results, the proposed method was applied to various types of news and non-news images, and it was found that the proposed method extracted text regions more accurately than existing methods. [ABSTRACT FROM AUTHOR]

Real-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods are comparatively low for a real USV system moving in a complicated environment. This paper proposes an efficient waterline detection method based on structure extraction and texture analysis with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of local binary patterns (LBPs) and gray level co-occurrence matrix (GLCM) were analyzed, and their advantages were integrated to calculate the texture information of river images. Then, structure extraction was introduced to preprocess the original river images so that the textures resulting from USV motion, wind, and illumination are removed. In the practical application, the waterlines of many images captured by the USV system moving along an inland river were detected with the proposed method, and the results were compared with those of edge detection and super pixel segmentation. The experimental results showed that the proposed algorithm is effective and robust. The average error of the proposed method was 1.84 pixels, and the mean square deviation was 4.57 pixels. [ABSTRACT FROM AUTHOR]

This paper presents two novel contrast enhancement approaches using texture regions-based histogram equalization (HE). In HE-based contrast enhancement methods, the enhanced image often contains undesirable artefacts because an excessive number of pixels in the non-textured areas heavily bias the histogram. The novel idea presented in this paper is to suppress the impact of pixels in non-textured areas and to exploit texture features for the computation of histogram in the process of HE. The first algorithm named as Dominant Orientation-based Texture Histogram Equalization (DOTHE), constructs the histogram of the image using only those image patches having dominant orientation. DOTHE categories image patches into smooth, dominant or non-dominant orientation patches by using the image variance and singular value decomposition algorithm and utilizes only dominant orientation patches in the process of HE. The second method termed as Edge-based Texture Histogram Equalization, calculates significant edges in the image and constructs the histogram using the grey levels present in the neighbourhood of edges. The cumulative density function of the histogram formed from texture features is mapped on the entire dynamic range of the input image to produce the contrast-enhanced image. Subjective as well as objective performance assessment of proposed methods is conducted and compared with other existing HE methods. The performance assessment in terms of visual quality, contrast improvement index, entropy and measure of enhancement reveals that the proposed methods outperform the existing HE methods. [ABSTRACT FROM AUTHOR]

Designing a robust image local descriptor for the purpose of image segmentation, analysis, recognition and classification has been an active area of research to date. In this paper, a robust and computationally efficient image local descriptor named 'COLOR CHILD' has been proposed. COLOR CHILD addresses the weaknesses of Weber Local Descriptor (WLD) by considering Laplacian of Gaussian (LoG) for its differential excitation component and Tiansi fractional derivative-based filter for its orientation component. For any given image, these two components are then used to construct a concatenated histogram and with the addition of color moments up to third order the capabilities of the proposed descriptor COLOR CHILD has been extended to handle textures in color space. COLOR CHILD is shown to outperform all of the known state-of-the-art image local descriptors of parametric and non-parametric types on a variety of benchmark texture databases such as KTH-TIPS2-a, KTH-TIPS2-b, and CUReT under varying degrees of noise while performing texture classification task. Further, the response profile of the COLOR CHILD in terms of Wasserstein distance measures (obtained by sliding a query patch across the image to be segmented) is found to be better suited as initial image for active contour-based image and texture segmentation algorithms. The efficacy of the COLOR CHILD for segmentation task is amply demonstrated on synthetic color images under varying degrees of noise and on real-world texture images. [ABSTRACT FROM AUTHOR]

Digital multitoning is an extension of halftoning for rendering more than two tones at each pixel for higher image quality. Although a lot of effort has been put in generating dispersed dots previously, the blue-noise feature can hardly be achieved for those printers utilizing the electrophotography (EP) process to avoid the physically unstable isolated dots. To overcome this issue, Chandu et al. proposed a screening method for yielding green-noise dot clusters, yet noisy multitone texture was accompanied. This degrades the visual quality and the stability of tone rendering. In this paper, a significantly improved homogeneity of clustered dots can be achieved by the proposed screening method based upon the new inter-iterative clustered-dot direct multi-bit search algorithm. Compared with the former approaches, the inter-iteration design leads to less error by the updated initial multitone patterns. As demonstrated in the experimental results, both of the high homogenous multitone texture and less noisy perception at all absorptance levels are offered in contrast to the former Chandu et al.’s results. The high-quality output proves it as a very competitive candidate for EP printers, e.g., laser printers. [ABSTRACT FROM PUBLISHER]

Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5?5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches. [ABSTRACT FROM AUTHOR]

This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples. [ABSTRACT FROM PUBLISHER]

Mass lesions are one of the breast cancer tumors. Mammogram images are the first screening tool to detect tumors in the women breast, but due to radiologist fatigue, number of false positive (FP) and false negative (FN) rates are increased. The main objective of this paper is to develop an intelligent computer aided diagnosis (CAD) system that can accurately detect mass lesions in digitized mammogram images. The proposed method has three stages. The first stage is a preprocessing stage, where the mass lesion is enhanced using a customized Laplacian filter. Then, multistatistical filters are implemented to detect a potential mass lesion in the mammogram images. In the final stage, the number detected FP regions are reduced using five texture features. The proposed algorithm is evaluated using 45 mammogram images and the algorithm achieved an accuracy rate of 97% in detecting mass lesion with 83% sensitivity rate and 98% specificity rate. [ABSTRACT FROM AUTHOR]

An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction. [ABSTRACT FROM AUTHOR]