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1
Girod, Luc, and Marc Pierrot-Deseilligny. "L'Égalisation radiométrique de nuages de points 3D issus de corrélation dense." Revue Française de Photogrammétrie et de Télédétection, no. 206 (June 19, 2014): 3–14. http://dx.doi.org/10.52638/rfpt.2014.90.
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Si les problèmes de colorimétrie dans le mosaïquage d'images ont fait l'objet d'études approfondies par le passé et qu'ils sont maintenant globalement résolus, il n'en est pas de même pour l'égalisation des scènes non planaires et des produits photogrammétriques 3D associés. En effet, certains produits photogrammétriques ne sont pas des images mais des produits purement 3D, de type nuage de points ou surfaces texturées, notamment. Cependant, la cohérence colorimétrique reste d'une grande importance dans ces cas pour une visualisation plus fluide des résultats. Cet article explore donc des algorithmes de correction colorimétrique à appliquer aux nuages de points dont la couleur provient de plusieurs images et leur implémentation dans la librairie MicMac de l'IGN.Deux points sont ici abordés : la correction du vignettage des images d'une part, ce défaut posant des problèmes d'homogénéité intra-image, et l'égalisation inter-images d'autre part.
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Boussidi, Brahim, Ronan Fablet, Emmanuelle Autret, and Bertrand Chapron. "Accroissement stochastique de la résolution spatiale des traceurs géophysiques de l'océan: application aux observations satellitaires de la température de surface de l'océan." Revue Française de Photogrammétrie et de Télédétection, no. 202 (April 16, 2014): 66–78. http://dx.doi.org/10.52638/rfpt.2013.52.
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Le développement des capteurs satellitaires d'observation des traceurs géophysiques à la surface de l'océan et les algorithmes de traitement associés ont connu un essor important au cours des vingt dernières années. Les différents capteurs satellitaires disponibles présentent des résolutions spatiales et temporelles différentes ainsi que différents niveaux de sensibilité à la couverture nuageuse. Dans le cas des images de température de surface de la mer, ceci se traduit notamment par de forts taux de données manquantes dans les observations de très haute-résolution (de l'ordre de 1kmx1km) contrairement aux observations de basse-résolution (de l'ordre de 25kmx25km). Il existe donc un enjeu fort pour exploiter conjointement les différentes sources d'information disponibles. Dans ce contexte, nous proposons un nouveau modèle stochastique de super-résolution basé sur une augmentation réaliste de l'information texturale des images. L'originalité de ce modèle réside dans la formulation d'a priori stochastiques sur la géométrie des images, qui sont caractéristiques des textures associées aux champs géophysiques à la surface de l'océan. Formellement, ce modèle consiste à modéliser les lignes de niveau de l'image comme des réalisations de marches aléatoires. Ce modèle stochastique s'étend naturellement à la simulation d'images haute-résolution à partir d'une observation basse-résolution. Cet article décrit la formulation mathématique du modèle proposé, ses caractéristiques théoriques ainsi que le schéma numérique mis en oeuvre pour la super-résolution d'images texturées. L'application à la simulation haute-résolution de champs de température de surface de la mer dans une région active de l'océan (courant des Aiguilles) démontre sa pertinence dans le contexte applicatif de la télédétection satellitaire de l'océan. Nous en discutons également les principales contributions ainsi que les différentes extensions possibles.
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Hemalatha, S., and S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection." International Journal of Ambient Computing and Intelligence 7, no. 2 (2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.
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This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.
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Bhaumik, Shubrajit, Viorel Paleu, Dhrubajyoti Chowdhury, et al. "Tribological Investigation of Textured Surfaces in Starved Lubrication Conditions." Materials 15, no. 23 (2022): 8445. http://dx.doi.org/10.3390/ma15238445.
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The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.
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Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias, and Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras." Sensors 21, no. 9 (2021): 3248. http://dx.doi.org/10.3390/s21093248.
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The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, capable of producing quality textures even in considerable camera misalignments scenarios. To this end, we argue that use of the depth data from RGB-D images can be an invaluable help to confer such robustness to the texture mapping process. Results show that the complete texture mapping procedure proposed in this paper is able to significantly improve the quality of the produced textured 3D meshes.
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Wen, Mingyun, Jisun Park, and Kyungeun Cho. "Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks." Remote Sensing 13, no. 21 (2021): 4254. http://dx.doi.org/10.3390/rs13214254.
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This study focuses on reconstructing accurate meshes with high-resolution textures from single images. The reconstruction process involves two networks: a mesh-reconstruction network and a texture-reconstruction network. The mesh-reconstruction network estimates a deformation map, which is used to deform a template mesh to the shape of the target object in the input image, and a low-resolution texture. We propose reconstructing a mesh with a high-resolution texture by enhancing the low-resolution texture through use of the super-resolution method. The architecture of the texture-reconstruction network is like that of a generative adversarial network comprising a generator and a discriminator. During the training of the texture-reconstruction network, the discriminator must focus on learning high-quality texture predictions and to ignore the difference between the generated mesh and the actual mesh. To achieve this objective, we used meshes reconstructed using the mesh-reconstruction network and textures generated through inverse rendering to generate pseudo-ground-truth images. We conducted experiments using the 3D-Future dataset, and the results prove that our proposed approach can be used to generate improved three-dimensional (3D) textured meshes compared to existing methods, both quantitatively and qualitatively. Additionally, through our proposed approach, the texture of the output image is significantly improved.
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Volkova, Natalya P., and Viktor N. Krylov. "VECTOR-DIFFERENCE TEXTURE SEGMENTATION METHOD IN TECHNICAL AND MEDICAL EXPRESS DIAGNOSTIC SYSTEMS." Herald of Advanced Information Technology 3, no. 4 (2020): 226–39. http://dx.doi.org/10.15276/hait.04.2020.2.
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The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in the field variations and quasi-harmonics. On its basis, a vector-difference method of texture segmentation has been developed, which is based on the vector transformation of images of spectral and statistical textures based on vector algebra. The stages of the vector-difference method are the following: an evaluation of the spectral texture feature; an evaluation of the statistical texture feature; vector-difference transformation of texture images; a boundary detection of the homogeneous regions. For each pixel of the image in the processing aperture, the features of the spectral and statistical texture are evaluated. Statistical texture evaluation was performed by the quadratic-amplitude transformation. At the stage of vector-difference transformation of texture images, a vector of features of each pixel of an image is constructed, the elements of which are estimates of features of a spectral and statistical texture, and the modulus of the difference of two vectors is calculated. At the stage of boundary detection of homogeneous regions, Canny method was applied. The developed vector-difference texture segmentation method was applied both to model images of spectral-statistical texture and to texture images obtained in technical and medical diagnostics systems, namely, for images of psoriasis disease and wear zones of cutting tools. To compare the segmentation results, frequency-detector and amplitude-detector methods of texture segmentation were applied to these images. The quality of segmentation of homogeneous textured regions was evaluated by the Pratt's criterion and by constructing a confusion matrix. The research results showed that the developed vector-difference texture segmentation method has increased noise tolerance at a sufficient processing speed.
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Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli, and Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification." Sensors 20, no. 18 (2020): 5432. http://dx.doi.org/10.3390/s20185432.
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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.
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Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri, and M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.9233.
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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.
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Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri, and M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.pp1152-1160.
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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.
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Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprsarchives-xli-b1-341-2016.
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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.
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Kang, Junhua, Fei Deng, Xinwei Li, and Fang Wan. "AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 341–47. http://dx.doi.org/10.5194/isprs-archives-xli-b1-341-2016.
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In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.
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Nishad, N., R. Meenakshi, R. Ramakrishnan, and A. Chirputkar. "Texture analysis for skin cancer diagnosis using dermoscopic images." CARDIOMETRY, no. 25 (February 14, 2023): 287–91. http://dx.doi.org/10.18137/cardiometry.2022.25.287-291.
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This paper provides a foundation to examine the dermoscopic images for skin cancer diagnosis. A dermoscopic image will often include textured areas that make up a major amount of the image. It is conceivable to organize and categorize such textures according to whether they are related with artifacts or if they reflect biological structure. Given the connection between structure, disease, and texture, it seems likely that quantitative measurements of texture might make it possible to characterize the tissues included inside a dermoscopic image. It has been shown that texture is a valuable characteristic for the characterization of skin cancer in dermoscopic images. The proposed system is comprised of two stages: the first is the extraction of information or features from dermoscopic images, and the second is the categorization of those images using a decision tree classifier. Based on the findings, it is possible to draw the conclusion that the extracted features have kept all of the information presents in the dermoscopic image that provides an overall accuracy of 98.89%
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Liu, Changhong, Hongyin Li, Zhongwei Liang, et al. "A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution." Electronics 11, no. 19 (2022): 3038. http://dx.doi.org/10.3390/electronics11193038.
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The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.
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Abdelmounaime, Safia, and He Dong-Chen. "New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis." ISRN Machine Vision 2013 (February 24, 2013): 1–14. http://dx.doi.org/10.1155/2013/876386.
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Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.
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Volkova, Natalya P., and Viktor N. Krylov. "HYBRID TEXTURE IDENTIFICATION METHOD." Herald of Advanced Information Technology 4, no. 2 (2021): 123–34. http://dx.doi.org/10.15276/hait.02.2021.2.
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The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice
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Fanany Onnilita Gaffar, Achmad, Darius Shyafary, Rony H, and Arief Baramanto Wicaksono Putra. "The new proposed method for texture modification of closed up face image based on image processing using local weighting pattern (LWP) with enhancement technique." International Journal of Engineering & Technology 7, no. 2.2 (2018): 94. http://dx.doi.org/10.14419/ijet.v7i2.2.12742.
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The texture is a two- and three-dimensional design element that is distinguished by the visual and physical properties perceived. Textured areas in the image can be marked with uniform or varying spatial intensity distribution. There are many techniques and methods from simple to sophisticated which available including machine learning-based methods to modify the texture map. The texture feature description becomes a new challenge in the field of computer vision and pattern recognition since the emergence of the local pattern binary method (LBP). This study proposes a new method called Local Weighting Pattern (LWP) for modifying textures based on the pixel's neighborhood of an RGB image. The results of this study obtained that LWP method produces a texture with a unique and artistic visualization. The Log function has been used to improve the image quality of the LWP method.
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Yang, Junxing, Lu Lu, Ge Peng, He Huang, Jian Wang, and Fei Deng. "Texture-Mapping Error Removal Based on the BRIEF Operator in Image-Based Three-Dimensional Reconstruction." Remote Sensing 15, no. 2 (2023): 536. http://dx.doi.org/10.3390/rs15020536.
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In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.
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Almeghari, Tamara A. I., Mohamed Soufiane Jouini, and Fawaz Hjouj. "Unsupervised texture classification of 3D X-ray Micro-computed Tomography images." Journal of Physics: Conference Series 2701, no. 1 (2024): 012143. http://dx.doi.org/10.1088/1742-6596/2701/1/012143.
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Abstract Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.
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Dal’Col, Lucas, Daniel Coelho, Tiago Madeira, Paulo Dias, and Miguel Oliveira. "A Sequential Color Correction Approach for Texture Mapping of 3D Meshes." Sensors 23, no. 2 (2023): 607. http://dx.doi.org/10.3390/s23020607.
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Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.
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Wang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan. "Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.
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Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.
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Coelho, Daniel, Lucas Dal’Col, Tiago Madeira, Paulo Dias, and Miguel Oliveira. "A Robust 3D-Based Color Correction Approach for Texture Mapping Applications." Sensors 22, no. 5 (2022): 1730. http://dx.doi.org/10.3390/s22051730.
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Texture mapping of 3D models using multiple images often results in textured meshes with unappealing visual artifacts known as texture seams. These artifacts can be more or less visible, depending on the color similarity between the used images. The main goal of this work is to produce textured meshes free of texture seams through a process of color correcting all images of the scene. To accomplish this goal, we propose two contributions to the state-of-the-art of color correction: a pairwise-based methodology, capable of color correcting multiple images from the same scene; the application of 3D information from the scene, namely meshes and point clouds, to build a filtering procedure, in order to produce a more reliable spatial registration between images, thereby increasing the robustness of the color correction procedure. We also present a texture mapping pipeline that receives uncorrected images, an untextured mesh, and point clouds as inputs, producing a final textured mesh and color corrected images as output. Results include a comparison with four other color correction approaches. These show that the proposed approach outperforms all others, both in qualitative and quantitative metrics. The proposed approach enhances the visual quality of textured meshes by eliminating most of the texture seams.
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Hu, Huiran, and Aiguo Song. "Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method." Applied Sciences 12, no. 23 (2022): 12346. http://dx.doi.org/10.3390/app122312346.
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The fractional differential algorithm has a good effect on extracting image textures, but it is usually necessary to select an appropriate fractional differential order for textures of different scales, so we propose a novel approach for haptic texture rendering of two-dimensional (2D) images by using an adaptive fractional differential method. According to the fractional differential operator defined by the Grünvald–Letnikov derivative (G–L) and combined with the characteristics of human vision, we propose an adaptive fractional differential method based on the composite sub-band gradient vector of the sub-image obtained by wavelet decomposition of the image texture. We apply these extraction results to the haptic display system to reconstruct the three-dimensional (3D) texture force filed to render the texture surface of two-dimensional (2D) images. Based on this approach, we carry out the quantitative analysis of the haptic texture rendering of 2D images by using the multi-scale structural similarity (MS-SSIM) and image information entropy. Experimental results show that this method can extract the texture features well and achieve the best texture force file for 2D images.
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Sobus, J., B. Pourdeyhimi, B. Xu, and Y. Ulcay. "Evaluating Loss of Texture Definition in Carpets Using Mathematical Morphology: Covariance." Textile Research Journal 62, no. 1 (1992): 26–39. http://dx.doi.org/10.1177/004051759206200105.
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Carpet textures contain periodic information that varies across constructions and is degraded by mechanical wear. We apply image covariance, a digital implementation of mathematical morphology, to binary carpet images for the purpose of measuring aspects of texture periodicity. Our test materials consist of four kinds of double ply wool carpets of differing textures divided into control, light, and heavy wear samples. Video images were digitized by a True Vision Vista frame grabber. Gray-level images were histogram equalized and converted to binary. Covariance data allow one to measure period frequency, amplitude, and overall mean. Results for our carpet samples show changes in amplitude and mean with wear, and are consistent with findings for a previous paper using grey level co-occurrence analysis. Covariance analysis requires relatively minimal computation for processing and preprocessing, but results may be affected by loss of gray level gradient information. If textural features of interest are preserved, this method is an efficient and easily implemented alternative to co-occurrence analysis. Attention is also given to the covariance analysis of computer generated carpet-like textures. We attempt to duplicate the covariance behavior of our carpet series by altering the placement of the component texture objects and simulate carpet wear by degrading regular textures with noise. We offer some thoughts on modeling carpet texture appearance loss with the aid of simulated texture images.
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Park, Juan, Chul Min Yeum, and Trevor Hrynyk. "Image Scale Estimation Using Surface Textures for Quantitative Visual Inspection." Journal of Computational Vision and Imaging Systems 6, no. 1 (2021): 1–3. http://dx.doi.org/10.15353/jcvis.v6i1.3541.
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In this study, a learning-based scale estimation technique is proposed to enable quantitative evaluation of inspection regions. The underlying idea is that surface texture of structures (i.e. bridges or buildings) captured on images contains the scale information of the corresponding images, which is represented by pixel per physical dimension (e.g., mm, inch). This allows training a regression model that provides a relationship between surface textures on images and their corresponding scales. Deep convolutional neural network is used to extract scale-related features from the texture patches and estimate their scales. The trained model can be exploited to estimate scales for all images captured from structure surfaces that have similar textures. The capability of the proposed technique is fully demonstrated using data collected from surface textures of three different structures and achieves an overall average scale estimation error of less than 15%.
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Okazawa, Gouki, Satohiro Tajima, and Hidehiko Komatsu. "Image statistics underlying natural texture selectivity of neurons in macaque V4." Proceedings of the National Academy of Sciences 112, no. 4 (2014): E351—E360. http://dx.doi.org/10.1073/pnas.1415146112.
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Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron’s preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.
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Knill, D. C. "From Contour to Texture: Static Texture Flow is a Strong Cue to Surface Shape." Perception 26, no. 1_suppl (1997): 205. http://dx.doi.org/10.1068/v970082.
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Contours projected from geodesic boundaries of developable surfaces (as are formed by folding and twisting flat surfaces) are particularly salient cues to 3-D surface shape. Textures which are strongly anisotropic (highly oriented) provide a similar source of information. The natural definition of homogeneity for such textures leads to the constraint that the oriented ‘flow’ of texture on a surface follows geodesics of the surface (on average). In the current work, it is shown that the shapes of contours projected from geodesics of developable surfaces, and analogously of oriented texture flow, reliably determine the shapes of the surfaces. On the basis of this analysis, it is suggested that human perception of surface shape from texture has two modes of operation: an isotropic mode, in which the visual system infers surface shape from local texture compression information, and a texture flow mode, in which the curvature of local texture flow determines local surface curvature, based on a geodesic constraint. In order to test the theory, planar texture patterns have been isometrically mapped with varying degrees of global orientation (ranging from isotropic to purely oriented) onto developable surfaces. The theory predicts that subjects' ability to make judgements about surface shape will be good for the isotropic textures and for highly oriented textures, but not for anisotropic textures that are only weakly oriented. As predicted, images of the surfaces with isotropic texture patterns induce strong percepts of shape, as do those of highly oriented textures. Images of anisotropic, weakly oriented patterns, however, elicit only weak percepts of shape.
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Khan, Haris, Sofiane Mihoubi, Benjamin Mathon, Jean-Baptiste Thomas, and Jon Hardeberg. "HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images." Sensors 18, no. 7 (2018): 2045. http://dx.doi.org/10.3390/s18072045.
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We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.
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Li, You Jiao, Tong Sheng Ju, and Meng Gao. "Texture Classification of 3D Surface Textures via Directional Quincunx Lifting." Applied Mechanics and Materials 686 (October 2014): 82–85. http://dx.doi.org/10.4028/www.scientific.net/amm.686.82.
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This thesis presents a new approach to classify 3D surface textures by using lifting transform with quincunx subsampling. Feature vectors are generated from eight different lifting prediction directions. We classify 3D surface texture images based on minimum Euclidean distance between the test images and the training sets. The feasibility and effectiveness of our proposed approach can be validated by the experimental results.
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Hu, Shirui, Zhiyuan Li, Shaohua Wang, Mingyao Ai, and Qingwu Hu. "A Texture Selection Approach for Cultural Artifact 3D Reconstruction Considering Both Geometry and Radiation Quality." Remote Sensing 12, no. 16 (2020): 2521. http://dx.doi.org/10.3390/rs12162521.
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3D reconstruction of culture artifacts has great potential in digital heritage documentation and protection. Choosing the proper images for texture mapping from multi-view images is a major challenge for high precision and high quality 3D reconstruction of culture artifacts. In this study, a texture selection approach, considering both the geometry and radiation quality for 3D reconstruction of cultural artifacts while using multi-view dense matching is proposed. First, a Markov random field (MRF) method is presented to select images from the best angle of view among texture image sets. Then, an image radiation quality evaluation model is proposed in the virtue of a multiscale Tenengrad definition and brightness detection to eliminate fuzzy and overexposed textures. Finally, the selected textures are mapped to the 3D model under the mapping parameters of the multi-view dense matching and a semi-automatic texture mapping is executed on the 3DMax MudBox platform. Experimental results with two typical cultural artifacts data sets (bronze wares and porcelain) show that the proposed method can reduce abnormal exposure or fuzzy images to yield high quality 3D model of cultural artifacts.
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Kinge, Sanjaykumar, B. Sheela Rani, and Mukul Sutaone. "Restored texture segmentation using Markov random fields." Mathematical Biosciences and Engineering 20, no. 6 (2023): 10063–89. http://dx.doi.org/10.3934/mbe.2023442.
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<abstract> <p>Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.</p> </abstract>
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Peng, Hong Tao, De Gan Zhang, Xiao Dong Song, and Xiang Wang. "Novel Synthesis Method for Image of Materials Texture." Applied Mechanics and Materials 687-691 (November 2014): 4140–47. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4140.
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Most patch-based texture synthesis algorithms using Markov Random Field for composite materials only considers color similarity between the corresponding pixels. The traditional algorithms are lack of adaptability, so the size of patches needs to be defined artificially in advance as the result of blurring of image texture features for composite materials. In order to improve above problems, a new patch-based sampling algorithm for synthesizing textures from an input sample image texture of composite materials is presented in this paper. By using patches of the sample texture as building blocks for image texture synthesis of composite materials, this algorithm makes high-quality texture synthesis for a wide variety of textures ranging regular to stochastic. The method is effective by our experimental results.
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Xu, Xin, and Yi Wang Chen. "Multifractal-Based Texture Analysis for SAR Images." Advanced Materials Research 915-916 (April 2014): 1216–20. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1216.
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SAR images are widely used in land use cover classifications. This paper proposed using multifractal spectrum parameters to analyze texture of SAR images. First multifractal theory and spectrum calculation method were introduced. Multifractal spectrum of a chaos representation was gotten with the method. From the spectrum curve, five parameters were found to describe the curve features. Then the physical meanings of five parameters in multifractal spectra were analyzed. Multifractal spectra of three kinds of textures in a SAR image are calculated, and five parameters are extracted. Finaly we analyzed the three textures according to physical meanings of five parameters. It is seen that the parameters of multifractal parameters can be used to describe different SAR image textures. It is a novel way to distinguish SAR image textures by multifractal spectrum parameters.
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Akl, Adib. "Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images." Cybernetics and Information Technologies 18, no. 1 (2018): 51–68. http://dx.doi.org/10.2478/cait-2018-0005.
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Abstract This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately representing the local variation of orientations in the underlying textured images, which paves the way towards an accurate analysis of the texture structures.
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Anjaiah, P., K. Rajendra Prasad, and C. Raghavendra. "Effective Texture Features for Segmented Mammogram Images." International Journal of Engineering & Technology 7, no. 3.12 (2018): 666. http://dx.doi.org/10.14419/ijet.v7i3.12.16450.
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Textures of mammogram images are useful for finding masses or cancer cases in mammography, which has been used by radiologist. Textures are greatly succeed for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) in most commonly used technique for mammogram segmentation. Limitation of this method is that it unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly for finding best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.
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Dong, Junyu, Jun Liu, Kang Yao, et al. "Survey of Procedural Methods for Two-Dimensional Texture Generation." Sensors 20, no. 4 (2020): 1135. http://dx.doi.org/10.3390/s20041135.
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Textures are the most important element for simulating real-world scenes and providing realistic and immersive sensations in many applications. Procedural textures can simulate a broad variety of surface textures, which is helpful for the design and development of new sensors. Procedural texture generation is the process of creating textures using mathematical models. The input to these models can be a set of parameters, random values generated by noise functions, or existing texture images, which may be further processed or combined to generate new textures. Many methods for procedural texture generation have been proposed, but there has been no comprehensive survey or comparison of them yet. In this paper, we present a review of different procedural texture generation methods, according to the characteristics of the generated textures. We divide the different generation methods into two categories: structured texture and unstructured texture generation methods. Example textures are generated using these methods with varying parameter values. Furthermore, we survey post-processing methods based on the filtering and combination of different generation models. We also present a taxonomy of different models, according to the mathematical functions and texture samples they can produce. Finally, a psychophysical experiment is designed to identify the perceptual features of the example textures. Finally, an analysis of the results illustrates the strengths and weaknesses of these methods.
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Chaudhary, R., RK Pandey, and SK Mazumdar. "Tribological studies of low and high viscous oils lubricated heavily loaded textured point contacts under the reciprocating motion." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 234, no. 2 (2019): 229–46. http://dx.doi.org/10.1177/1350650119858240.
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The tribo-performance behaviors of lubricated textured point contacts have been explored herein at relatively high contact loads (up to 3 GPa) under reciprocating motion (0.2 and 0.4 m/s) employing low (ν@40 ℃ = 100 cSt) and high (ν@40 ℃ = 422 cSt) viscous oils. In this experimental study, two contacts (flat conventional surface vs. polished ball and textured flat surface vs. polished ball surface) have been created for the investigations of friction, wear, and contact potential (ability for film formation). It is found that in the presence of texture at the concentrated contacts, the coefficient of friction and wear have reduced considerably with high viscous oil irrespective of operating parameters. However, low viscous oil has yielded an increase in the wear under identical operating parameters. The contact potential (an indirect indication of film formation during the running-in period) in the presence of texture develops rapidly as compared to the conventional surface with both oils. It has demonstrated a reduction in the running-in period in the presence of textures at the contacts. The optical microscope images of worn surfaces of balls and tracks have also been presented for the quantification of wear and understanding of the associated mechanisms.
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Chang, Lihong, Wan Ma, Yu Jin, and Li Xu. "An Image Decomposition Fusion Method for Medical Images." Mathematical Problems in Engineering 2020 (July 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/4513183.
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A fusion method based on the cartoon+texture decomposition method and convolution sparse representation theory is proposed for medical images. It can be divided into three steps: firstly, the cartoon and texture parts are obtained using the improved cartoon-texture decomposition method. Secondly, the fusion rules of energy protection and feature extraction are used in the cartoon part, while the fusion method of convolution sparse representation is used in the texture part. Finally, the fused image is obtained using superimposing the fused cartoon and texture parts. Experiments show that the proposed algorithm is effective.
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Qiao, Shuang, Jia Ning Sun, Jian Li, and Ji Peng Huang. "A Novel Texture Extraction Method for Digital Radiography." Applied Mechanics and Materials 719-720 (January 2015): 1148–54. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.1148.
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As known, there always exist severely degradation problems in digital radiography. How we can extract necessary textures from degraded radiographic images is the post-processing key. Local binary pattern (LBP) is a well-known method, which is widely used in fast image texture extraction. However, for noisy images, LBP can’t work well due to its sensitivity to details. On the other hand, as one of the important shock filters developed in recent years, complex shock filter possesses excellent capabilities in textural image processing. Here, by combining complex shock filter with LBP, a novel fast and efficient method, C-LBP is presented for texture extraction of degraded radiographic images. Experimental results show that comparing with traditional LBP, C-LBP not only distinguishes between noise and details in radiographic images, but also extracts image textures efficiently and rapidly, which plays an important role in developing nondestructive detection technique by low-dose ray radiography.
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Ropelewska, Ewa. "The application of image processing for cultivar discrimination of apples based on texture features of the skin, longitudinal section and cross-section." European Food Research and Technology 247, no. 5 (2021): 1319–31. http://dx.doi.org/10.1007/s00217-021-03711-3.
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AbstractThe study was aimed at the evaluation of the usefulness of textures of the outer surface from the images of apple skin and flesh for discrimination of different cultivars. The texture parameters were calculated from color channels: R, G, B, L, a, b, U, V, H, S, I, X, Y, Z. In the case of cultivar discrimination performed for the apple skin, the highest accuracies were obtained for textures from channels R, a and X. In the case of channels R and a, the apples were classified with the total accuracy of up to 93%. For channel X, the highest total accuracy was 90%. For discrimination based on the textures selected from images of a longitudinal section of apples, the total accuracy reached 100% for channels G, b and U. In the case of the cross-section images, the total accuracies were also satisfactory and reached 93% for channel G, 97% for channels b and U. The obtained results proved that the use of image processing based on textures can allow the discrimination of apple cultivars with a high probability of up to 100% in the case of textures selected from images of a longitudinal section. The results can be applied in practice for cultivar discrimination and detection of the falsification of apple cultivars. The obtained results revealed that texture features can allow for cultivar identification of apples with a very high probability in an inexpensive, objective, and fast way. Graphic abstract
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Chen, Jinsong, Hu Han, and Shiguang Shan. "Towards High-Fidelity Face Self-Occlusion Recovery via Multi-View Residual-Based GAN Inversion." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 294–302. http://dx.doi.org/10.1609/aaai.v36i1.19905.
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Face self-occlusions are inevitable due to the 3D nature of the human face and the loss of information in the projection process from 3D to 2D images. While recovering face self-occlusions based on 3D face reconstruction, e.g., 3D Morphable Model (3DMM) and its variants provides an effective solution, most of the existing methods show apparent limitations in expressing high-fidelity, natural, and diverse facial details. To overcome these limitations, we propose in this paper a new generative adversarial network (MvInvert) for natural face self-occlusion recovery without using paired image-texture data. We design a coarse-to-fine generator for photorealistic texture generation. A coarse texture is computed by inpainting the invisible areas in the photorealistic but incomplete texture sampled directly from the 2D image using the unrealistic but complete statistical texture from 3DMM. Then, we design a multi-view Residual-based GAN Inversion, which re-renders and refines multi-view 2D images, which are used for extracting multiple high-fidelity textures. Finally, these high-fidelity textures are fused based on their visibility maps via Poisson blending. To perform adversarial learning to assure the quality of the recovered texture, we design a discriminator consisting of two heads, i.e., one for global and local discrimination between the recovered texture and a small set of real textures in UV space, and the other for discrimination between the input image and the re-rendered 2D face images via pixel-wise, identity, and adversarial losses. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods in face self-occlusion recovery under unconstrained scenarios.
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Lu, Dengsheng, and Mateus Batistella. "Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon." Acta Amazonica 35, no. 2 (2005): 249–57. http://dx.doi.org/10.1590/s0044-59672005000200015.
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Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation.
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Goyal, Aparna, and Reena Gunjan. "Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review." E3S Web of Conferences 170 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202017003007.
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Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.
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Suresh Kumar, R., and A. R. Mahesh Balaji. "Land use land cover classification using local multiple pattern from very high resolution satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 971–76. http://dx.doi.org/10.5194/isprsarchives-xl-8-971-2014.
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The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.
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He, Haiqing, Jing Yu, Penggen Cheng, et al. "Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping." Remote Sensing 14, no. 1 (2021): 50. http://dx.doi.org/10.3390/rs14010050.
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Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.
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Abramov, Aleksei, Sergej M. Bobrovskij, Nikolay Nosov, Vladimir Tabakov, and Fanyusa Lopatina. "Method for Determining Texture Parameters of Processed Precision Surfaces by Correlation." Key Engineering Materials 822 (September 2019): 731–36. http://dx.doi.org/10.4028/www.scientific.net/kem.822.731.
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The article describes a new method for texture analysis of precision machined surfaces, which is based on the use of computer optics and an autocorrelation method for processing the obtained images of the textures of the studied microreliefs. The method is based on a probabilistic comparative assessment of the unknown texture of the studied microrelief with known textures of the reference microreliefs, for which the parameters of the microreliefs are predetermined according to the state standards of the Russian Federation.
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Men, Peng, Hao Guo, Jubai An, and Guanyu Li. "An Improved L2Net for Repetitive Texture Image Registration with Intensity Difference Heterogeneous SAR Images." Remote Sensing 14, no. 11 (2022): 2527. http://dx.doi.org/10.3390/rs14112527.
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Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.
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Gimel'Farb, Georgy L., and Anil K. Jain. "On retrieving textured images from an image database." Pattern Recognition 29, no. 9 (1996): 1461–83. http://dx.doi.org/10.1016/0031-3203(96)00011-8.
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Sang, Ruijuan, Adam John Manley, Zhihui Wu, and Xinhao Feng. "Digital 3D Wood Texture: UV-Curable Inkjet Printing on Board Surface." Coatings 10, no. 12 (2020): 1144. http://dx.doi.org/10.3390/coatings10121144.
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Natural wood textures are appreciated in most forest products industries for their appealing visual characteristics including grain and color, but also their fine surface tactile sensation. The following presents an ultraviolet (UV)-curable inkjet technology printing 3D wood texture on wood-based substrate by image processing and surface treatment. The UV printing was created from scanned digital images of a real wood surface and processed in graphics software. The images were converted to grayscale graphics by selecting color range and setting the parameter of fuzziness. The grayscale images were printed as 3D texture height simulation on the substrates and coated by printing the color images as texture mapping. Based on these wood texture digital images, the marquetry art is also considered in the images processing design to increase the artistry of the printed materials. The medium-density fiberboard (MDF) coated printing marquetry surface replicate realistic natural 3D wood texture surface layers on wood-based panels and imitated the effect of handcrafted wood art works. This study proves that printing 3D texture surface material is creative and valuable with ecologically friendly, low-consumption UV-curable inkjet technology and provides a feasible and scalable approach in flooring/furniture/decorative architectural panels.
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Song, Qiang. "Affine Texture Analysis with Scale-Area Histogram." Key Engineering Materials 474-476 (April 2011): 1183–86. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1183.
Texto completoResumen
A major problem of texture analysis is that textures in the real world are often not uniform due to variations in orientation, scale, or other visual appearance. In this paper, affine texture analysis with texel scale-area histogram is presented. A textural image is decomposed into a set of scale images and each scale image consists of square texels of the same size. The scale-area histogram of texel is used as texture feature for multi-scale texture analysis and dominant texture scale analysis. Measurement of the dominant texel sizes of textural images with different rotation angles and spatial scales indicates that rotational and scaled transformations of textural image result in the motion of translation in scale-area histogram.