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Texture synthesis techniques require nearly uniform texture samples, however identifying suitable texture samples in an image requires significant data preprocessing. To eliminate this work, we introduce a fully automatic pipeline to detect dominant texture samples based on a manifold generated using the diffusion distance. We define the characteristics of dominant texture and three different types of outliers that allow us to efficiently identify dominant texture in feature space. We demonstrate how this method enables the analysis/synthesis of a wide range of natural textures. We compare textures synthesized from a sample image, with and without dominant texture detection. We also compare our approach to that of using a texture segmentation technique alone, and to using Euclidean, rather than diffusion, distances between texture features. [ABSTRACT FROM AUTHOR]

Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the "non-stego" image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods. [ABSTRACT FROM AUTHOR]

The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method. We recently proposed a method based on the interpolated grey-level co-occurrence matrix (iGLCM), robust to image blur and noise but slower than the Fourier-based method. Here we test the applicability of convolutional neural networks (CNNs) to texture directionality detection. To obtain the large amount of training data required, we built a training dataset consisting of synthetic textures with known directionality and varying perturbation levels. Subsequently, we defined and tested shallow and deep CNN architectures. We present the test results focusing on the CNN architectures and their robustness with respect to image perturbations. We identify the best performing CNN architecture, and compare it with the iGLCM, the Fourier and the local gradient orientation methods. We find that the accuracy of CNN is lower, yet comparable to the iGLCM, and it outperforms the other two methods. As expected, the CNN method shows the highest computing speed. Finally, we demonstrate the best performing CNN on real-life images. Visual analysis suggests that the learned patterns generalize to real-life image data. Hence, CNNs represent a promising approach for texture directionality detection, warranting further investigation. [ABSTRACT FROM AUTHOR]

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield. [ABSTRACT FROM AUTHOR]

Abstract-We present a novel technique for synthesizing textures over dynamically changing fluid surfaces. We use both image textures, as well as bump maps as example inputs, Image textures can enhance the rendering of the fluid by either imparting a realistic appearance to it or by stylizing it, whereas bump maps enable the generation of complex microstructures on the surface of the fluid that may be very difficult to synthesize using simulation. To generate temporally coherent textures over a fluid sequence, we transport texture information, that is, color and local orientation, between free surfaces of the fluid from one time step to the next. This is accomplished by extending the texture information from the first fluid surface to the 3D fluid domain, advecting this information within the fluid domain along the fluid velocity field for one time step and interpolating it back onto the second surface-this operation, in part, uses a novel vector advection technique for transporting orientation vectors. We then refine the transported texture by performing texture synthesis over the second surface using our `surface texture optimization" algorithm, which keeps the synthesized texture visually similar to the input texture and temporally coherent with the transported one. We demonstrate our novel algorithm for texture synthesis on dynamically evolving fluid surfaces in several challenging scenarios. [ABSTRACT FROM AUTHOR]

L'objectif de ce travail est de tester l'apport de l'information texture‐couleur à la classification des images SPOT (une seule date) pour l'établissement de cartes d'occupation du sol. Souvent la texture et la couleur sont abordées d'une façon complètement séparée. Dans ce travail, elles sont intégrées dans un seul modèle dit modèle de texture‐couleur, afin d'étendre efficacement l'analyse de la texture pour les images multispectrales. Pour cela, la représentation habituelle de la texture, fondée sur la prise en compte des variations locales de contraste, est enrichie par l'adjonction d'une connaissance sur ses variations spectrales. Afin de tester l'apport de cette information, deux classifications ont été réalisées sur des images HRV (XS) de SPOT de la région d'Oran prise comme zone test pour la variété des thèmes en présence et son relief relativement accidenté. La première est purement spectrale et la seconde utilise en plus des canaux bruts, ceux de la texture‐couleur. Les résultats montrent, qu'en plus d'une amélioration significative de 18.9% du taux global de bonne classification obtenu par rapport à celui de 75.9% donné par l'approche purement spectrale, l'information de texture‐couleur a permis une meilleure discrimination entre les thèmes présentant une importante confusion après classification.Texture and colour are often used in a separated scheme without consideration of their mutual interactions. In this work they are combined in an integrative approach using the wavelet transformation technique. For that, texture–colour features are defined as wavelet coefficients covariance after colour space transformation. Colour–texture new channels are then used in addition to the radiometric channels to conduct a supervised classification process.In order to asses the texture–colour contribution, two classifications have been performed on the HRV (XS) SPOT images over the city of Oran (west of Algeria) taken as a test zone for its thematic variety, heterogeneity and relatively uneven topography. The first approach uses only spectral information while the second combines the radiometric and texture–colour information in a multi‐resolution classification scheme.Results show that the proposed approach brings significant improvement of 18.9% to the global classification rate compared to 75.9% given by the spectral approach. The texture–colour feature allowed better discrimination of confused items. [ABSTRACT FROM AUTHOR]

Thirty-two large cooked beef joints were clustered into tender and tough using k-means clustering based on the shear values obtained from an Instron machine. An image acquisition system was set up to capture images of beef samples, and four different groups of texture features, i.e., wavelet features (WF), Gabor features (GF), wavelet Gabor features (WGF), and a combination of wavelet features and Gabor features (CWG), were extracted from the captured images. After removing the collinear redundancy using principal component analysis (PCA) among the various features in each group, a linear discrimination function was employed for classifying the tenderness of the samples. The discrimination results, together with the error rates estimated by the posterior probability error rate estimate method, suggest that the WGF provided the best classification results, followed by WF and CWG. The GF performed the worst among the four methods. Some existing problems with the wavelet transform and Gabor filter, which limited the accuracy of the proposed method, were also discussed. [ABSTRACT FROM AUTHOR]

A divergence operator to measure the texture content in a multi‐spectral image is proposed. A multi‐spectral image is modelled as an n ‐dimensional vector field, ‘ n ’ being the number of bands of the image. A pixel of the image is an n ‐dimensional vector in this field. It is demonstrated that the flux variations of the vector field are related to changes in texture in the image. A divergence operator measures the flux variation and, hence, texture. In order to save computer memory, speed up the divergence operator calculation and lessen the content of noise of the image, principal component transformation is applied to the bands of the image. The first three principal components are used to span the vector field. The partial derivatives involved in the divergence operator are written as weighted finite differences. To estimate these derivatives, cubes of three, five and seven voxels per side are considered. The cube is systematically displaced to cover the entire domain of the vector field. In each position of the cube, the divergence value is calculated using the weighted finite difference approximation. This value is written as a pixel in an output image file according to the Cartesian coordinates defined by the location of the cube. This image file depicts the texture variations of the multi‐spectral image. The relation flux variation versus coarseness of texture is discussed. Two examples, based on Landsat Thematic Mapper multi‐spectral and synthetic multi‐spectral images, are presented and analysed. [ABSTRACT FROM AUTHOR]

In this study, a segmentation procedure is proposed, based on grey‐level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey‐scale and colour textures, depicted by accuracy values of 96% and 98%, respectively. The second case study involved segmentation of coastal land cover objects from a multi‐spectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP‐based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty. [ABSTRACT FROM AUTHOR]

Texture synthesis has been an active research area in computer graphics, vision, and image processing. A hybrid texture synthesis approach that combines the strength of Ashikhmin?s [1] and Liang?s [11] algorithms is presented in this paper. Using patches from the input sample texture to extend the input itself, more suitable valid candidate pixels in the input sample can be more precisely determined. An extra original position array is used to record the valid pixel position in the extended region of input sample texture such that the candidate pixel can be retrieved efficiently. Pasting patches with more global information of texture features to initialize the output texture, the texture synthesis process tends to grow larger pieces in the output texture so that the perceptual similarity is reserved as much as possible. Many classes of textures have been used to test our approach. As the experimental results showed, the proposed hybrid approach effectively and efficiently synthesizes high-quality textures from a wide variety of textures. [ABSTRACT FROM AUTHOR]

This work presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and desaturation is performed in the u'v'Y color space, to change the saturation value in the chromaticity diagram, and adaptive histogram equalization then is adopted to adjust the luminance component. Additionally, this work presents a new patching method using the Markov Random Field (MRF) model of texture synthesis. Eliminating undesirable aged painting patterns, such as stains, crevices, and artifacts, and then filling the lacuna regions with the appropriate textures is simple and efficient. The synthesization procedure integrates three key approaches, weighted mask, annular scan and auxiliary, with neighborhood searching. These approaches can maintain a complete shape and prevent edge disconnection in the final results. Moreover, the boundary between original and synthesized paintings is seamless, and unable to distinguish in which the undesirable pattern appears. [ABSTRACT FROM AUTHOR]

The aim of this work was to develop a system based multifeature texture analysis and modular neural networks at will facilitate the automated interpretation of satellite cloud Such a system will provide a standardized and efficient way for classifying cloud types that can be used as an operational tool in weather analysis. A series of 98 infrared satellite images from the geostationary satellite METEOSAT7 were employed, and 366 cloud segments were labeled into six cloud types after the combined agreed observations from ground and satellite. From segmented cloud images, nine different texture feature sets (a total of 55 features) were extracted, using the following algorithms: statistical features, spatial gray-level dependence matrices, gray-level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws' 'texture energy measures, fractals, and Fourier power spectrum. The neural network self-organizing feature map (SOFM) classifier and the classification of the cloud images. Furthermore, the classification results of the nine different feature sets were combined, improving the classification yield for the six classes, for the SOFM classifier to 61% and for the KNN classifier to 64%. [ABSTRACT FROM AUTHOR]

Many investigators have demonstrated that texture data improve the precision of a classification when they are combined with grey level information. However, the computation of textural parameters of order 2 often takes far too long. The problem is more complex when we have to compute textural parameters of order 3, which can grant a better precision in comparison with parameters of a lower order. A method, developed by Kourgli and Belhadj-Aissa allows a reduction in the computation time of the evaluation of second order textural parameters, based on the grey level co-occurrence matrix. This method is based on a technique allowing a double summation to pass to a simple summation. We have pursued the study further by developing textural parameters of order 3, showing how it is possible to express them by a simple summation instead of a triple summation. This method has been developed and tested with a Synthetic Aperture Radar (SAR) ERS-1 image, on a computer equipped with a Pentium processor of 800 MHz. The results obtained show that the new method of computation is at least two times faster than the classical method of computation of the co-occurrence grey level matrices. [ABSTRACT FROM AUTHOR]

This paper develops a new technique for modeling and classifying a growing texture using its evolution function over time. It encompasses morphological texture classification and parameter estimation with the objective of assessing the state of growth achieved by the texture using only a small sample set to train on, consistent with many real world situations for quality control. It is assumed that the texture model evolves over time according to the way in which its evolution function determines the parameters of its defining random process. This paper considers the random Boolean model for both binary and gray-scale images. A multiple linear regression model is used to estimate the Boolean model parameters as functions of the granulometric moments of the textures. Once the texture-model parameters are estimated, the time of the process can be found via the manner in which the parameters are determined by the dynamic evolutionary model. [ABSTRACT FROM AUTHOR]

This paper investigates the application of support vector machines (SVMs) texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architecture, while also providing solutions to problems inherent in these methods. One-against-others decomposition is adopted to apply binary SVMs to multitexture classification, plus a neural network is used as an arbitrator to make final classifications from several one-against-others SVM outputs. Experimental results demonstrate the effectiveness of SVMs in texture ciassification. [ABSTRACT FROM AUTHOR]

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max-min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max-min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability, and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems. [ABSTRACT FROM AUTHOR]

We propose a new model of texture images using morphological filters. This method is based on a linear predictive model [autoregressive (AR) model], which is a representative texture image model. This method embeds the frequency characteristics of the texture image in the AR model. In this paper, we will demonstrate a new model that represents the spatial features of a texture image. Morphological filters can remove the spatial features and are used for pattern spectra. In this paper, we consider embedding the spatial features in the structuring elements of morphological filters and propose an Opening Model (OM) to model the convex up features and a Closing Model (CM) to model the convex down features. We examine the reliability of the proposed model and present improvements to the low-reliability model. Furthermore, our recognition tests clearly show excellent recognition when OM and CM are used together. © 2001 Scripta Technica, Electron Comm Jpn Pt 3, 84(7): 53–62, 2001 [ABSTRACT FROM AUTHOR]

Presents a mathematical definition of texture, the Julesz ensemble which is the set of all images that share identical statistics. Two significant facts in texture modeling and synthesis; Review of some important image features and statistics used in texture modeling; Information on the Gibbs ensemble and ensemble equivalence.

Describes a proposed model for color texture synthesis-by-analysis method. Other approaches for the synthesis of grey-level textures; Characteristics of textures; Components of texture; Illustration of the three channel and two channel color texture modeling; Details on the parameter identification procedure.

Accurate landform classification is a crucial component of geomorphology. Although extensive classification efforts have been exerted based on the terrain factor, the scale analysis to describe the macro and micro landform features still needs standard measurement. To obtain the appropriate analysis scale of landform structure feature, and then carry out landform classification using the terrain texture, the texture feature is introduced for reflecting landform spatial differentiation and homogeneity. First, applying the ALOS World 3D-30m (AW3D30) DEM and selecting typical landforms of the southwest Tibet Plateau, the discrete wavelet transform (DWT), which acts as the texture feature analysis method, is executed to dissect the multiscale structural features of the terrain texture. Second, through the structural indices of reconstructed texture images, the optimum decomposition scale of DWT is confirmed. Under these circumstances, wavelet coefficients and wavelet energy entropy are extracted as texture features. Finally, the random forest (RF) method is utilized to classify the landform. Results indicate that the texture feature of DWT can achieve higher classification accuracy, which increases by approximately 11.8% compared with the gray co-occurrence matrix (GLCM). [ABSTRACT FROM AUTHOR]

In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel. [ABSTRACT FROM AUTHOR]

The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models' quality for the test datasets equals 0.85 and 0.73 for PM10 and 0.63 for PM2.5. The quality of each classification model differs (0.86 and 0.73 for PM10, and 0.80 for PM2.5). The obtained results show that the created classification models could be used in PM10 and PM2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth. [ABSTRACT FROM AUTHOR]

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images. [ABSTRACT FROM AUTHOR]

A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is introduced first. It creates an effective scale-space by using a well-posed anisotropic diffusion filtering model that is proposed and approximated numerically here. A feature extraction process using a bank of circularly symmetric 2D filters is applied at each scale, then a rotation-invariant texture feature vector is achieved for the current image by combining the feature vectors computed at all these scales. Next, a weighted similarity graph, whose vertices correspond to the texture feature vectors and the weights of its edges are obtained from the distances computed between these vectors, is created. A novel weighted graph clustering technique is then applied to this similarity graph, to determine the texture classes. Numerical simulations and method comparisons illustrating the effectiveness of the described framework are also discussed in this work. [ABSTRACT FROM AUTHOR]

Presents information on a study which focused on a twin stage texture synthesis-by-analysis method. Aims of the twin stage method; How the binary textural behavior is represented; Information on the binary hard-limited Gaussian process; Findings of the study.

Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works. [ABSTRACT FROM AUTHOR]

Currently, there is an increasing demand for wooden furniture and products, especially in the decorating business where wood textures are widely used. These textures have different artificial designs, and because different consumers have different needs for wood texture, the trend of using computer algorithms to design wood textures emerged. We propose a method for synthesizing wood's heterogeneous texture. It can analyze the characteristics of different wood textures, select the most appropriate input sample block size, and then generate a new image with a sample texture. Compared with the deep learning method, our method reduces pressure on system resources and production costs. The proposed method also generates higher quality reconstructed images than traditional algorithms. [ABSTRACT FROM AUTHOR]

Windthrow and storm damage are crucial issues in practical forestry. We propose a method for rapid detection of windthrow hotspots in airborne digital orthophotos. Therefore, we apply Haralick's texture features on 50 × 50 m cells of the orthophotos and classify the cells with a random forest algorithm. We apply the classification results from a training data set on a validation set. The overall classification accuracy of the proposed method varies between 76% for fine distinction of the cells and 96% for a distinction level that tried to detect only severe damaged cells. The proposed method enables the rapid detection of windthrow hotspots in forests immediately after their occurrence in single-date data. It is not adequate for the determination of areas with only single fallen trees. Future research will investigate the possibilities and limitations when applying the method on other data sources (e.g., optical satellite data). [ABSTRACT FROM AUTHOR]

In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the performance of CNNs and other state-of-art Machine Learning (ML) algorithms: (1) performance of CNN using bands, (2) performance of Random Forest (RF), support vector regression (SVR), artificial neural network (ANN) on bands, and vegetation indices (VIs). (3) Performance of RF, SVR, and ANN on gray-level co-occurrence matrices (GLCM), and exploratory spatial data analysis (ESDA), and (4) performance of RF, SVR, and ANN based on all combined data and ESDA+VIs. CNNs reached satisfactory results (with R2 = 0.943) even with limited input variables (i.e., only bands). In comparison, RF and SVR with elaborately designed data obtained slightly better accuracy than CNN. For examples, RF based on GLCM textures reached an R2 of 0.979 and RF based on all combined data reached a close R2 of 0.974. However, the results of ANN were much worse (with the best R2 of 0.885). [ABSTRACT FROM AUTHOR]

Background: The accurate estimation of rice LAI is particularly important to monitor rice growth status. Remote sensing, as a non-destructive measurement technology, has been proved to be useful for estimating vegetation growth parameters, especially at large scale. With the development of unmanned aerial vehicles (UAVs), this novel remote sensing platform has been widely used to provide remote sensing images which have much higher spatial resolution. Previous reports have shown that the spectral feature of remote sensing images could be an effective indicator to estimate vegetation growth parameters. However, the texture feature of high-resolution remote sensing images is rarely employed for this purpose. Besides, the physical mechanism between the texture feature and vegetation growth parameters is still unclear. Results: In this study, a Fourier spectrum texture based on the UAV Image was developed to estimate rice LAI. And the relationship between Fourier spectrum texture and rice LAI was also analyzed. The results showed that Fourier spectrum texture could improve the accuracy of rice LAI estimation. Conclusions: In conclusion, the texture feature of high-resolution remote sensing images may be more effective in rice LAI estimation than the spectral feature. [ABSTRACT FROM AUTHOR]

Open areas, along with their non-forest vegetation, are often threatened by secondary succession, which causes deterioration of biodiversity and the habitat's conservation status. The knowledge about characteristics and dynamics of the secondary succession process is very important in the context of management and proper planning of active protection of the Natura 2000 habitats. This paper presents research on the evaluation of the possibility of using selected methods of textural analysis to determine the spatial extent of trees and shrubs based on archival aerial photographs, and consequently on the investigation of the secondary succession process. The research was carried out on imagery from six different dates, from 1971 to 2015. The images differed from each other in spectral resolution (panchromatic, in natural colors, color infrared), in original spatial resolution, as well as in radiometric quality. Two methods of textural analysis were chosen for the analysis: Gray level co-occurrence matrix (GLCM) and granulometric analysis, in a number of variants, depending on the selected parameters of these transformations. The choice of methods has been challenged by their reliability and ease of implementation in practice. The accuracy assessment was carried out using the results of visual photo interpretation of orthophotomaps from particular years as reference data. As a result of the conducted analyses, significant efficacy of the analyzed methods has been proved, with granulometric analysis as the method of generally better suitability and greater stability. The obtained results show the impact of individual image features on the classification efficiency. They also show greater stability and reliability of texture analysis based on granulometric/morphological operations. [ABSTRACT FROM AUTHOR]

The perception of textures is based on high-level features such as symmetry, brightness, color or direction. Texture characterization is a widely studied topic in the image processing community. The normalized volume of morphological series is used as a texture descriptor in RGB images. However, the correlation between different color channels is not exploited with this descriptor. We propose the usage of inter-channel measures in addition to the volume, to enhance the descriptors potential to discriminate textures. The experiments show that standard texture classification techniques increase between 3%–10% in performance when using our descriptor instead of other state of the art descriptors that do not use inter-channel measures. [ABSTRACT FROM AUTHOR]

Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as "crunchiness" and "crispness". Experimental results validate the model's capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed. [ABSTRACT FROM AUTHOR]

First a novel sketch filter based on the maximum filter is proposed. The proposed sketch filter implicitly calculates the complexity of local area, and results in an image which expresses edges and textures as an artist draws a sketch with a pencil. It is then proposed to apply the sketch filter for edge feature extraction, colour pencil drawing, and the animation filtered image. The proposed algorithms work automatically without any interaction and demonstrate impressive results for rendering applications, while requiring a much smaller amount of computations compared to existing schemes. [ABSTRACT FROM AUTHOR]

Texturing an animated fluid is a useful way to augment the visual complexity of pictures without increasing the simulation time. But texturing flowing fluids is a complex issue, as it creates conflicting requirements: we want to keep the key texture properties (features, spectrum) while advecting the texture with the underlying flow—which distorts it. In this paper, we present a new, Lagrangian, method for advecting textures: the advected texture is computed only locally and follows the velocity field at each pixel. The texture retains its local properties, including its Fourier spectrum, even though it is accurately advected. Due to its Lagrangian nature, our algorithm can perform on very large, potentially infinite scenes in real time. Our experiments show that it is well suited for a wide range of input textures, including, but not limited to, noise textures. [ABSTRACT FROM PUBLISHER]