Real Texture Research Articles

Tactile Perception of Virtual Edges and Gratings Displayed by Friction Modulation via Ultrasonic Actuation.

Tactile discrimination and roughness perception of real textures are extensively studied and underlying perceptual mechanisms are relatively well-established. However, tactile perception of virtual textures rendered by friction modulation techniques on touch surfaces has not been investigated in detail yet. In this article, we investigated our ability to discriminate two consecutive step changes in friction (called edges), followed by discrimination and roughness perception of multiple edges (called periodic gratings). The results showed that discrimination of two consecutive edges was significantly influenced by edge sequence: a step fall in friction ( FF) followed by a step rise in friction ( RF) was discriminated more easily than the reverse order. On the other hand, periodic gratings displayed by consecutive sequences of FF followed by RF were perceived with the same acuity as compared to vice versa. Independent of the edge sequence, we found that a relative difference of 14% in spatial period was required to discriminate two periodic gratings. Moreover, the roughness perception of periodic gratings decreased with increasing spatial period for the range that we have investigated (spatial period 2mm), despite the lack of spatial cues on grating height. We also observed that rate of change in friction coefficient was better correlated with the roughness perception than the friction coefficient itself. These results will further help to understand and design virtual textures for touch surfaces.

Presenting Surface Features Using a Haptic Ring: A Psychophysical Study on Relocating Vibrotactile Feedback.

In the context of wearable technologies, it is often important for the fingers to be unconstrained so that they can be used to explore the environment. In this paper, we explored the feasibility of presenting vibrotactile cues that represented different textures to one of three locations on the hand and forearm using a wearable device. The first experiment indicated that vibrotactile signals of varying frequency rendered by the tactile display could be encoded by participants in terms of changes along a roughness-smoothness dimension. The differential thresholds measured for vibrotactile frequency were significantly higher on the wrist as compared to the fingerpad and the distal phalanx of the index finger. In two subsequent experiments vibrotactile signals were presented by a tactile ring worn on the distal phalanx and participants evaluated real textures explored by the fingerpad and virtual textures rendered by the ring. It was found that participants could compare and rank in terms of roughness two spatially distributed inputs with reasonable accuracy. In the context of the haptic ring being developed, these findings indicated that it is feasible to display information experienced at the fingertip on a more proximal location on the hand, thus freeing the fingers for other tasks.

PHOTOGRAMMETRIC STUDY OF THE QUBBET EL-HAWA COPTIC CHURCH (ASWAN, EGYPT)

Abstract. This paper presents the method applied and the results obtained in the photogrammetric study of the Qubbet el-Hawa Coptic Church. After several archaeological interventions, the ruins of this church were completely uncovered of sand. Therefore, a geometric documentation is justified in prevision of possible damages or deterioration that could affect it, considering the weakened status of several structures. In this context, we selected close-range photogrammetry because of the site conditions which include several vertical walls and occlusions that complicated the use of other terrestrial methods. In addition, a real texture of the object was demanded in order to obtain a complete documentation of the church. In this sense, some decorations and wall paintings were still visible causing this requisite of documentation based on real textures. However, the use of terrestrial photogrammetry was complicated due to the presence of occlusions, and in addition the use of Remotely Piloted Aircraft Systems (RPAS) was not allowed. So a method using a mast of about 4 to 6 metres was considered in order to lift the camera, avoiding these obstacles and reducing the amount of photographs needed. Applying this method, a great set of photographs was acquired and the scene covered with convergent images. The results of the photogrammetric processes were several metric products such as a 3D model with real textures, a ground Digital Elevation Model (DEM), several orthoimages, sections, etc. In addition a preliminary virtual reconstruction of the church has been developed based on these results. In conclusion, we have obtained a complete and reliable geometric documentation of the site and other derived products that can be used for other purposes. The method used has demonstrated its efficiency in these types of studies.

Data-Driven Texture Modeling and Rendering on Electrovibration Display

With the introduction of variable friction displays, either based on ultrasonic or electrovibration technology, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic texture rendering on an electrovibration display. We first describe a motorized linear tribometer designed to collect lateral frictional forces from textured surfaces under various scanning velocities and normal forces. We then propose an inverse dynamics model of the display to describe its output-input relationship using nonlinear autoregressive neural networks with external input. Forces resulting from applying a pseudo-random binary signal to the display are used to train each network under the given experimental condition. In addition, we propose a two-step interpolation scheme to estimate actuation signals for arbitrary conditions under which no prior data have been collected. A comparison between real and virtual forces in the frequency domain shows promising results for recreating virtual textures similar to the real ones, also revealing the capabilities and limitations of the proposed method. We also conducted a human user study to compare the performance of our neural-network-based method with that of a record-and-playback method. The results showed that the similarity between the real and virtual textures generated by our approach was significantly higher.

Yapı ve Anlam Bakımından Klasik Türk Şiirinde Arkaizm Örnekleri

Language is both the preserver and transmitter of a culture. Cultural elements are retained and transmitted to the future thanks to written language. The richest period of Turkish literature in terms of written works is doubtlessly the Classical Turkish Literature Period. Texts from Classical Turkish Literature are rich sources not only for literary people but also for linguists. It is possible to track the vocabulary, grammatical structures and archaic elements of the Turkish language from the texts of classical Turkish literature. However, language structure has changed and even structures far removed from the real texture of the language sometimes emerged as classical Turkish literature extended over many centuries. It is necessary to properly understand the classical Turkish poetry and to know the characteristics of the historical periods of the Turkish language to examine and analyze these structures more effectively. In this study, the focus was seventeen archaic terms and idioms (ağzına sögmek, alaçlık olmak, ancalayın, arkalanmak, aylandurmak, baş koşmak, başına topraklar saçmak, bun demi, cân kefde baş etekte, cân yidmek, çığrışmak, ege komak, el kavşurmak, el yumak, gönlekcek, kan yalaşmak, tevbeyi sımak) determined in Hayretî’s Divan. The determined terms and idioms were reviewed in other divans and examples of couplets were reproduced. Terms and idioms were analyzed in terms of roots and structures, examples of couplets were translated within the language and the meaning frames of the related terms and idioms and the concepts they were used in were shown.

Authoring New Haptic Textures Based on Interpolation of Real Textures in Affective Space

This paper presents a novel haptic texture authoring algorithm. The main goal of this algorithm is to synthesize new virtual textures by manipulating the affective properties of already existing real-life textures. To this end, two different spaces are established: two-dimensional (2-D) “affective space” built from a series of psychophysical experiments where real textures are arranged according to affective properties (hard-soft, rough-smooth) and 2-D “haptic model space” where real textures are placed based on features from tool-surface contact acceleration patterns (movement-velocity, normal-force). Another space, called “authoring space” is formed to merge the two spaces; correlating changes in affective properties of real-life textures to changes in actual haptic signals in haptic space. The authoring space is constructed such that features of the haptic model space that were highly correlated with affective space become axes of the space. As a result, new texture signals corresponding to any point in authoring space can be synthesized based on weighted interpolation of three nearest real surfaces in perceptually correct manner. The whole procedure including the selection of nearest surfaces, finding weights, and weighted interpolation of multiple texture signals are evaluated through a psychophysical experiment, demonstrating the competence of the approach. The results of evaluation experiment show an average normalized realism score of 94 $\%$ for all authored textures.

Diverse adversarial network for image super-resolution

Recently, there is a fast growth in Generative adversarial network and many works have appeared focusing not only images but also videos. Despite of remarkable success of GAN in image super resolution, it suffers from the major problem of poor perceptual quality. While employing a GAN for super resolution, it tends to generate over-smoothed images that lacks high frequency textures and do not look natural. We propose an intuitive generalization to Generative Adversarial Network and its conditional variations to address the problem of image super-resolution and improves the test quality of images. DGAN is a diverse GAN architecture incorporating multiple generators and a single discriminator. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. To enforce that multiple generators produce diverse samples, the discriminator trains a loss function to distinguish between real and fake samples by designed margins, and multiple generators alternately produce realistic samples by minimizing their losses. In fact, this paper addresses 2 main challenges; recovering realistic texture low resolution images and speed up the training process. We perform extensive experiments and compare the proposed model with other variants of GAN to demonstrate the efficiency and stability of the proposed model in both quantitative and qualitative benchmarks.

Disentangling Geometry and Appearance with Regularised Geometry-Aware Generative Adversarial Networks

Deep generative models have significantly advanced image generation, enabling generation of visually pleasing images with realistic texture. Apart from the texture, it is the shape geometry of objects that strongly dictates their appearance. However, currently available generative models do not incorporate geometric information into the image generation process. This often yields visual objects of degenerated quality. In this work, we propose a regularized Geometry-Aware Generative Adversarial Network (GAGAN) which disentangles appearance and shape in the latent space. This regularized GAGAN enables the generation of images with both realistic texture and shape. Specifically, we condition the generator on a statistical shape prior. The prior is enforced through mapping the generated images onto a canonical coordinate frame using a differentiable geometric transformation. In addition to incorporating geometric information, this constrains the search space and increases the model’s robustness. We show that our approach is versatile, able to generalise across domains (faces, sketches, hands and cats) and sample sizes (from as little as sim , 200{-}30{,}000 to more than 200, 000). We demonstrate superior performance through extensive quantitative and qualitative experiments in a variety of tasks and settings. Finally, we leverage our model to automatically and accurately detect errors or drifting in facial landmarks detection and tracking in-the-wild.

Touchy : A Visual Approach for Simulating Haptic Effects on Touchscreens

Haptic enhancement of touchscreens usually involves vibrating motors producing limited sensations or custom mechanical actuators that are difficult to disseminate. In this paper, we propose an alternative approach called Touchy, where a symbolic cursor is introduced under the user's finger, to evoke various haptic properties through changes in its shape and motion. This novel metaphor enables to address four different perceptual dimensions, namely: hardness, friction, fine roughness and macro roughness. Our metaphor comes with a set of seven visual effects that we compared with real texture samples within a user study conducted with 14 participants. Taken together our results show that Touchy is able to elicit clear and distinct haptic properties: stiffness, roughness, reliefs, stickiness and slipperiness.

PENCIPTAAN LUKISAN BERBASIS BENTUK SENI RUPA ETNIK KARO PADA MATRA KERET YANG DIBENTUK DENGAN ALAT PENGANTAR PANAS

The article, a result of this research, discusses the creation of art painting based on ethnic visual art of Karo, which is expressed on rubber material dimension formed with heat conductor device. The purpose is to find the form of North Sumatera’s visual art which is particular and unique. Therefore, it can contribute to the enrichment of the local and national cultural value. The method used is survey method and creative creation method. The testing of the result of the creation was done through the assessment of competent critics and appreciative response of the observers and the community. The data analysis was conducted with the quantitative and qualitative descriptive method. The result of this research shows that the character of Karo ethnic’s visual art as the basis of the art paintings’ development is commonly decorative and mythical. Rubber material as screen area with heat conductor is quite effective to explore the real texture and relief image, as well as producing artistic effect when exposed with light. Based on the critics’ and the observers’ appreciative response, the created works have had the quality of intra-aesthetic and extra-aesthetic, strong uniqueness and characteristics, hence it can contribute to enriching the local and national culture.

Unsupervised texture reconstruction method using bidirectional similarity function for 3-D measurements

The developments of three-dimensional (3-D) imaging and display technology have created important demands for 3-D model with realistic texture. Due to texture misalignment and inconsistent illumination, texture reconstruction is subject to degradation like blurring, ghosting and color discontinuity. A composite weight texture blending approach is proposed to mitigate the artifacts. For inaccurate geometric model, bidirectional similarity function based multi-scale energy minimization method is proposed to correct texture misalignment. The average processing time of the proposed method is only 40% of existing method. The optical experiments demonstrate that photorealistic texture could be achieved for inaccurate geometric model and inconsistent illumination.

Discriminative Features for Texture Retrieval Using Wavelet Packets

Wavelet Packets (WPs) bases are explored seeking new discriminative features for texture indexing. The task of WP feature design is formulated as a learning decision problem by selecting the filter-bank structure of a basis (within a WPs family) that offers an optimal balance between estimation and approximation errors. To address this problem, a computationally efficient algorithm is adopted that uses the tree-structure of the WPs collection and the Kullback-Leibler divergence as a discrimination criterion. The adaptive nature of the proposed solution is demonstrated in synthetic and real data scenarios. With synthetic data, we demonstrate that the proposed features can identify discriminative bands, which is not possible with standard wavelet decomposition. With data with real textures, we show performance improvements with respect to the conventional Wavelet-based decomposition used under the same conditions and model assumptions.

AAGAN: Enhanced Single Image Dehazing With Attention-to-Attention Generative Adversarial Network

Due to the atmospheric scattering and absorption, hazy weather often occurs in our everyday life, thus reducing the visibility of scenes. Single image dehazing is considered as an ill-posed and challenging problem in computer vision. To restore visibility in inclement weather, we propose an attention-to-attention generative adversarial network (AAGAN) whose motivation is the human visual perceptual mechanism. More specifically, a dense channel attention model is embedded into the encoder. Moreover, its output is projected forward to the corresponding multiscale spatial attention model in the decoder. Both attention models form an attention-to-attention mechanism to implement attention projection, thus capturing global feature dependencies of the whole network. Besides, we analyze the dehazing mechanism based on the atmospheric scattering model, and then utilize an improved RaLSGAN to recover more realistic texture information and enhance visual contrast for different hazy scenes. Finally, in order to improve the visual performance of image restoration, we remove all the instance normalization layers to avoid unnecessary artifacts, and then introduce spectral normalization for all the convolution layers to stabilize the entire training process. Qualitative assessments and analyses demonstrate that our proposed approach can achieve remarkable dehazing performance on both synthetic and real-world scenes against previous state-of-the-art methods.

Learning-Based Texture Synthesis and Automatic Inpainting Using Support Vector Machines

Texture synthesis methods based on patch sampling and pasting can generate realistic textures with a similar appearance to a small sample. However, the sample usually has to be used throughout the synthesis stage. In contrast, the learned representation of the textures is more compact and discriminative, and can also yield good synthesis results. In this paper, we introduce a learned approach for texture synthesis based on support vector machines (SVM). This approach benefits from the merit of SVM that the sample texture pattern is learned using a model, and the sample itself can be discarded during the synthesis stage; the approach is also used to synthesize three-dimensional surface textures. Experimental results show that our approach is particularly effective in modeling and synthesizing near-regular or regular textures, which are difficult to achieve using traditional parametric texture synthesis methods. We further apply the proposed approach to constrained texture synthesis, image extrapolation, and texture inpainting. For texture inpainting, we develop a new method for automatically detecting holes in textures without the requirement of human intervention. Our approach yields promising results for the three tasks.

Controllable Image‐Based Transfer of Flow Phenomena

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

La arqueología de órdenes militares en Castilla-La Mancha y la reconstrucción virtual de su patrimonio

<p><strong>Extended Abstract:</strong></p><p>The objective of this paper is to show the virtual archaeology as one of the methodologies applicated in the research line “Archaeology of the military orders” developed for some years by the University of Castilla-La Mancha in collaboration with the Fundación Castillo de la Estrella.</p><p>The military orders are very important institutions for the study of the Middle Ages in the Iberian Peninsula. Because of that, there is a rich literature which approaches many aspects of these institutions: their origins and evolution; organisation; economic dimension; social reality... In contrast, the studies which have focused on the heritage linked to these orders have not had the same degree of development. This lack has motivated the creation of this important research line, where several specialists from different disciplines such as history, archaeology, architecture or restoration try to focus on the research of the military orders heritage, mainly in the region of Castilla-La Mancha. Thus, our research line is based on an interdisciplinary methodology, combining traditional practice with new technologies, like the virtual archaeology, which is described as the “using computer-based visualisation for the comprehensive management of archaeological heritage” (Principios de Sevilla, 2012).</p><p><em>Virtual archaeology as a research methodology</em></p><p>There are several applications for the virtual archaeology, but we could summarize them in three: research, conservation and restoration, and communication. In this paper, we focused on the first of them. We have worked with virtual archaeology in several cases such as the Castle of La Estrella (Montiel, Ciudad Real), the fortress and priory of Uclés (Uclés, Cuenca) or the hospital of Santiago of Alarcón (Alarcón, Cuenca), and we can say that this methodology has contributed to progress in the knowledge of all the elements which have been recreated. In the creation process of a virtual model, it is necessary to collect all information and data as are possible of the element on which we work. In this process, a lot of questions about several aspects appears, and we must try to find responses. In this way, we discover things which probably never would have been approached without this process. Furthermore, the virtual archaeology is an excellent method to sketch and discuss different hypothesis. It is a visual language with whom the specialists could show their ideas as support of the traditional text formats or other graphics sources as photos or plans.</p><p><em>Workflow</em></p><p>Our workflow is similar toot her projects of virtual archaeology. Before to start to work, is important to think about the objective of the model. For example, there are a lot of differences between a simple model to sketch the possible spatial disposition of a building in the research discussion, and a recreation to show to the public. Then, we can start to work in a process which can be summarized in these steps:</p><ul><li><p>Compilation of all the documents, information and data as are possible about the element to recreate.</p></li><li><p>Discussion about several aspects of the model as the plan, materials, constructive technics, decoration, landscape, etc. Is interesting to use sketches or drawings before starting to work in the 3D model.</p></li><li><p>Design of the 3D model starting with the general aspects as the spatial disposition of the different elements, its size or the main details, and after that, work with the specific elements, decorations or contextual items (in recreations).</p></li><li><p>Texturize the different objects of the model. It is important to work with the correct materials. In this way, we try to use the real textures of the archaeological elements when we have them.</p></li><li><p>Integrate the model in a correct context: topography, landscape, people and animals, furniture, etc. These aspects will be present with a different degree of detail depending on the objective of the virtual model.</p></li><li><p>The last step is the creation of various sources as images, videos, interactive application, etc</p></li><li><p>To preserve the scientific transparency is important to show the degree of evidence of the different elements of the model. An option is to use a colour scale like the one which has been developed by Aparicio & Figueiredo (2016) which reflects the origin of the data and the degree of historical and archaeological evidence that we have.</p></li></ul><p>It is important to point out that the discussion between the specialists must be present in all these steps.</p><p><em>Conclusions</em></p><p>The virtual archaeology is a growing methodology in the heritage management sphere. As a communication tool, it is an excellent language to show ideas to the general and the specialised public. But this has many possibilities in other fields such as conservation and restoration, and research. As with any other methodology, it is necessary to create scientific criteria and rules for it use, a process which has already started with the creation of the Seville Principles. Now, is the turn of the researches and heritage specialists to do a correct use of this tool and develop its multiple possibilities.</p>

Automatic texture exemplar extraction based on global and local textureness measures

Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.

Reconstruction of deformed microstructure using cellular automata method

The kinetics of microstructural evolution phenomena like recrystallization, grain growth, and phase transformation of deformed materials is affected by the characteristics of deformed microstructure. In fact, average grain size, grain morphology, texture and grain boundary properties of the deformed material determine the microstructure characteristics. In this paper, the reconstruction of deformed microstructure and changes in the microstructure in mesoscale are studied. Accordingly, the normal growth, topology deformation, and reconstruction of texture and grain boundary misorientation techniques are used to reconstruct the deformed microstructure. Therefore, probabilistic cellular automata method with hexagonal cells is used to create a microstructure with equiaxed grain morphology followed by a new modified topology deformation technique. In this technique, the quality and the quantity of the plastic deformation are considered by applying the deformation gradient tensor to the undeformed microstructure. Finally, a set of crystal orientations is created using a probabilistic algorithm relevant to the real texture of the material and then the crystal orientations are assigned to the deformed grains in such a way that satisfy the misorientation angle distribution. The accuracy of the numerical approaches is verified by comparing the experimental and the simulated results.

Interactive segmentation of texture image based on active contour model with local inverse difference moment feature

Texture image segmentation is a challenging problem in image processing field due to wide variability of characterizing textures and a lack of proper contour information. In this paper, an effective presentation is found with which different textures can be represented with some corresponding distributions generated by feature values of local inverse difference moment (LIDM). By the analysis of local statistical information in gray level co-occurrence matrix (GLCM), we found that similar textures can be characterized with similar distributions. In this way, an interactive segmentation method is presented to achieve the segmentation of texture image based on GLCM with an optimizing model. Our scheme can be narrated separately as follows, firstly, a proper Gaussian kernel is selected to discriminate two classes of textures by analyzing LIDM feature distributions, which are obtained from two different local regions marked manually in the initial texture image. Secondly, the LIDM feature map can be constructed by computing LIDM feature values of image patches with the proper Gaussian kernel, and the center of these image patches may traverse the whole image domain. Finally, the texture image segmentation is implemented based on an improved optimizing model with local binary fitting and local extremum regularizing. In order to validate the performance of our proposed method, two kinds of experiments about discriminative feature map construction and texture image segmentation are carried out to demonstrate its well performance, and more experiments on real texture images are also conducted.

Improved imaging and analysis of chlorite in reservoirs and modern day analogues: new insights for reservoir quality and provenance

Abstract Chlorite is a key mineral in the control of reservoir quality in many siliciclastic rocks. In deeply buried reservoirs, chlorite coats on sand grains prevent the growth of quartz cements and lead to anomalously good reservoir quality. By contrast, an excess of chlorite – for example, in clay-rich siltstone and sandstone – leads to blocked pore throats and very low permeability. Determining which compositional type is present, how it occurs spatially, and quantifying the many and varied habits of chlorite that are of commercial importance remains a challenge. With the advent of automated techniques based on scanning electron microscopy (SEM), it is possible to provide instant phase identification and mapping of entire thin sections of rock. The resulting quantitative mineralogy and rock fabric data can be compared with well logs and core analysis data. We present here a completely novel Quantitative Evaluation of Minerals by SCANning electron microscopy (QEMSCAN®) SEM–energy-dispersive spectrometry (EDS) methodology to differentiate, quantify and image 11 different compositional types of chlorite based on Fe : Mg ratios using thin sections of rocks and grain mounts of cuttings or loose sediment. No other analytical technique, or combination of techniques, is capable of easily quantifying and imaging different compositional types of chlorite. Here we present examples of chlorite from seven different geological settings analysed using QEMSCAN® SEM–EDS. By illustrating the reliability of identification under automated analysis, and the ability to capture realistic textures in a fully digital format, we can clearly visualize the various forms of chlorite. This new approach has led to the creation of a digital chlorite library, in which we have co-registered optical and SEM-based images, and validated the mineral identification with complimentary techniques such as X-ray diffraction. This new methodology will be of interest and use to all those concerned with the identification and formation of chlorite in sandstones and the effects that diagenetic chlorite growth may have had on reservoir quality. The same approach may be adopted for other minerals (e.g. carbonates) with major element compositional variability that may influence the porosity and permeability of sandstone reservoirs.