Visual Texture Research Articles

The taste of visual textures

Numerous crossmodal correspondences between visual elements and basic tastes have been documented in recent years. Research has shown that many of these correspondences can influence taste expectations when applied in food packaging. However, research on correspondences between visual textures and tastes is scarce, despite the ability of the former to convey specific information about materials and objects. In the present study, we conducted two online experiments designed to study crossmodal correspondences between basic taste words and visual textures with common material properties. In Experiment 1 (N = 194), we evaluated explicit associations between six visual texture categories (with four levels of each category) and basic taste words. The results revealed moderate associations between one of the fluffy visual textures and sweetness and between a rough and a crunchy visual texture and saltiness. In Experiment 2 (N = 407), we superimposed the visual textures associated with the basic tastes found in Experiment 1 on food extrinsic factors (i.e., packaging, napkin) served in combination with products of three taste qualities (i.e., neutral/ambiguous, sweet, salty). We did not find evidence supporting the idea that visual textures that are crossmodally corresponding to specific tastes, as revealed in Experiment 1, influenced taste expectations. The results of the study suggest that the strength of the crossmodal correspondence between visual textures and basic taste words studied here is moderate.

Development and characterization of edible biofilms based on mucilage of Opuntia ficus-indica and Locust Bean Gum from Tissemsilt region in Algeria

The development of biodegradable edible films/food packaging is in the core of the new management strategies of food handling. The great challenge facing the food industry today is innovation in terms of ecological and effec-tive edible coatings, with improved mechanical and sensory qualities but at lower cost. The current study intends to develop and characterize edible films by combining crude and pure Opuntia cladode mucilage and Locust Bean Gum (LBG), then evaluate their coating effect on stored strawberry fruits. The films were formulated using the two polymers at the same con-centration (2% mucilage / 2% LBG) and glycerol as plasticizer, and they were compared for their physicochemical and mechanical properties like thick-ness, moisture content, water vapor transmission and transparency. The pH, weight loss and sensorial qualities of coated strawberries stored at 4°C were assessed during 14 days. The obtained results have shown that for the crude composite films CM-CLBG the thickness, moisture and water solubility were higher compared to those of PM-PLBG where water vapor permeability (WVP), transparency and surface contact angle (hydrophobicity) displayed the highest values. Weight loss and drop in pH of strawberries soaked in the CM-CLBG and PM-PLBG coating solutions and stored at 4°C were less signifi-cant than those of uncoated strawberries. Otherwise, coated fruits pre-served a marketable visual appearance and texture, but lost their redness and gloss after the storage period. Despite the promising potential of Opun-tia mucilage/LBG formulated films, some improvements are still needed to solve the problem of fruit appearance and meet marketing standards.

Modeling visual aesthetic perception: bridges between computed texture features and perceived beauty qualities in semantic experiments.

The exploration of the potential relationship between computable low-level texture, such as features extracted from color and texture, and the perceived high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. First, the selection and clustering of aesthetic antonyms used to represent the aesthetic properties of visual texture are completed through two semantic differential experiments. Subsequently, 151 visual textures are rated according to the selected aesthetic antonyms by participants in a third semantic differential experiment. Third, 106 textural features are extracted using four different image analysis algorithms to describe the low-level characteristics of visual textures. Finally, the construction and evaluation of the visual aesthetic perception model based on multiple linear and nonlinear regression algorithms are discussed. We analyzed the frequency of each aesthetic antonym selected from 20 pairs of semantic antonyms, and the most frequently mentioned 8 pairs of semantic antonyms were selected as the core set for model building. The extracted low-level features are highly correlative. Of the correlation coefficients based on absolute values, 3383 are less than 0.75, accounting for 14.84% of the total. The correlation coefficients were larger than 0.5 accounts for 27.29% of the total. Through neighborhood component analysis, the top 10 low-level features are selected with lower correlation. The gap between low-level calculated features and high-level perceived aesthetic emotions can be bridged by a brain-inspired model of visual aesthetic perception.

Quantification of Visual Texture and Presentation of Intermediate Visual Texture by Spatial Mixing.

We proposed a method to display an intermediate visual texture by spatial mixing. In addition to color information, the visual texture is an important element that characterizes the nature of an object’s surface. While the system to display various color information has well matured in engineering, there is no method to reproduce visual textures in ambient light. In our method, the matte and glossy surfaces are used as “primary visual textures”, and an intermediate visual texture is displayed by spatially mixing the primary visual textures. In this paper, we first quantified the visual texture of an object’s surface based on measured intensities of scattered and reflected lights. Next, based on the quantification, we evaluated spatially mixed surfaces consisting of two primary visual textures, an acrylic plate and a holed sheet of drawing paper, by changing the area proportion of the two primary visual textures. Finally, a sensory evaluation showed significant differences between each intermediate visual texture, and the results corresponded to a trend in the optical evaluation. This study illustrates that visual textures could be quantified based on the intensity of scattered and reflected light and reveals the applicability of our method to the display for intermediate visual texture.

Making digital aquatint with the Ising model

Aquatint is a printmaking technique that gives visual texture to prints. This patterning is shown to be similar to patterns that emerge from the Ising model of magnetism. We provide a concrete Python-based example that generates semi-realistic textures and can be used to manipulate existing images. Our experience in classrooms of first year seminar undergraduate students shows that this activity helps artistically inclined students to develop an interest in physics and computer science. It may also be suitable for a joint art and science project for high-school seniors.

Effects of Roughness and Texture on Surface Material Perception in Virtual Environments: The Psychophysics Approach

Effects of Roughness and Texture on Surface Material Perception in Virtual Environments: The Psychophysics Approach

Detection of coronavirus disease using texture analysis and machine learning methods

The recent outbreak of the novel SARS-CoV-2 virus (COVID-19) has caused serious problems across the world. Patients with such diseases can have severe symptoms and may die. The early diagnosis of COVID-19 may reduce the death rate. Chest X-ray technology is one of the good low-cost diagnostic tools in analysing such diseases. However, its accurate detection is becoming prone to serious errors caused by the low radiographic contrast. In this paper, we address the problem of data classification using texture features and machine learning along with using AI algorithms. The aim is to show that it is possible to take advantage of both visual texture descriptors and AI methods to accurately diagnose COVID-19. An evaluation process was conducted on real datasets showing the merits of developed framework. The other aim is to show the robustness of texture features in solving the current problem. We validated the developed models on different datasets and we evaluate their performance in terms of various metrics. Through extensive experiments, we prove the merits of the current work.

Reading, Attention, and Poetic Suggestion: ‘Éventail (de Madame Mallarmé)’

Abstract Much like the shift from print to digital media today, the expansion of cheaper, more portable literature in nineteenth-century France led many readers to regard texts as destined for rapid consumption rather than careful contemplation. Responding to this development, Stéphane Mallarmé fashioned his poetry to resist casual reading and solicit halting, iterative processing instead. Scholars usually see this laboured engagement as resulting from his fragmented syntax, arcane vocabulary, and the number of phonetic, metrical, and lexical patterns competing for readers’ attention. However, as the following analysis of ‘Éventail (de Madame Mallarmé)’ demonstrates, the difficulty of reading Mallarmé’s work is also a function of the visible properties of the text — not only the typography and white spaces discussed in studies of Un coup de dés jamais n’abolira le hasard, but punctuation, line breaks, and even the patterning of letters. Adopting terminology from cognitive science to identify Mallarmé’s innovative tactics, this article explains how the visual texture of his poetry interacts with its other features (aural, syntactic, semantic, and so on) to interrupt the continuity of reading and enhance the poetics of suggestion for which he is celebrated.

Analysis of visual impression based on Bra's basic components

PurposeThe purpose of this paper is to discuss the relationship between bra's visual impression and bra parts, and then to explore the application of visual impression in bra design.Design/methodology/approachFirstly, 82 female undergraduates are asked to answered this questionnaire online, which is about the importance of parts in bra design. In the part of data analysis, the method of principal part analysis (PCA) are used to get the relationship between bra's parts, and reduce dimension of factors that influence bra design. After that, those group of features are further discussed from the perspective of visual design. Finally, design an application based on conclusion.FindingsTo get the influence features of bra appearance and improve the visual design effect, this paper matches the bra parts with visual features (color, texture, shape and space) and presents four main features of bra design: “color,” “visual texture,” “design shape” and “spatial expression” together with corresponding bra parts and technique of expression. Moreover, user interface in bra cloud customization is designed.Practical implicationsThe conclusion, which shows the corresponding relationship between bra visual effect and its basic parts, has an important role in bra visual design. First, it can be useful for design idea with different technique of expression, which may supply a theoretical basis for design. Secondly, the combination of bra parts and visual features can be used to evaluate the appearance.Originality/valueDiscussing the bra visual impression based on bra's basic parts and visual features provides a theoretical method for bra design and its appearance evaluation.

Image retrieval-based parenchymal analysis for breast cancer risk assessment.

This research on breast cancer risk assessment aims to develop models that predict the likelihood of breast cancer. In recent years, the computerized analysis of visual texture patterns in mammograms, namely parenchymal analysis, has shown great potential for risk assessment. However, the visual complexity and heterogeneity of visual patterns limit the performance of parenchymal analysis in large populations. In this work, we propose a method to create individualized risk assessment models based on the radiological visual appearance (radiomic phenotypes) of the mammograms. We developed a content-based image retrieval system to stratify mammographic analysis according to the similarities of their radiomic phenotypes. We collected 1144 mammograms from 286 women following a case-control study design. We compared the classical parenchymal analysis with the proposed approach using the area under the ROC curve (AUC) with 95% confidence intervals (CI). Statistical significance was assessed using DeLong's test ( 0.05). At a patient level, AUC values of 0.504 (95% CI: 0.398-0.611) with classical parenchymal analysis increased to 0.813 (95% CI: 0.734-0.892) when the radiomic phenotypes are incorporated with the proposed method. In risk estimation from individual, standard mammographic views, the highest performance was obtained with the mediolateral oblique view of the right breast (RMLO), with an AUC value of 0.727 (95% CI: 0.634-0.820). Differences in performance among views were statistically significant ( ) CONCLUSIONS: These results indicate that the utilization of radiomic phenotypes increases the performance of computerized risk assessment based on parenchymal analysis of mammographic images. The creation of individualized risk assessment models may be leveraged to target personalized screening and prevention recommendations according to the person'srisk.

Perceptual Texture Dimensions Modulate Neuronal Response Dynamics in Visual Cortical Area V4.

Texture is an important visual attribute for surface pattern discrimination and therefore object segmentation, but the neural bases of texture perception are largely unknown. Previously, we demonstrated that the responses of V4 neurons to naturalistic texture patches are sensitive to four key features of human texture perception: coarseness, directionality, regularity, and contrast. To begin to understand how distinct texture perception emerges from the dynamics of neuronal responses, in 2 macaque monkeys (1 male, 1 female), we investigated the relative contribution of the four texture attributes to V4 responses in terms of the strength and timing of response modulation. We found that the different feature dimensions are associated with different temporal dynamics. Specifically, the response modulation associated with directionality and regularity was significantly delayed relative to that associated with coarseness and contrast, suggesting that the latter are fundamentally simpler feature dimensions. The population of texture-selective neurons could be grouped into multiple clusters based on the combination of feature dimensions encoded, and those subpopulations displayed distinct temporal dynamics characterized by the weighted combinations of multiple features. Finally, we applied a population decoding approach to demonstrate that texture category information can be obtained from short temporal windows across time. These results demonstrate that the representation of different perceptually relevant texture features emerge over time in the responses of V4 neurons. The observed temporal organization provides a framework to interpret how the processing of surface features unfolds in early and midlevel cortical stages, and could ultimately inform the interpretation of perceptual texture dynamics.SIGNIFICANCE STATEMENT To delineate how neuronal responses underlie our ability to perceive visual textures, we related four key perceptual dimensions (coarseness, directionality, regularity, and contrast) of naturalistic textures to the strength and timing of modulation of neuronal responses in area V4, an intermediate stage in the form-processing, ventral visual pathway. Our results provide the first characterization of V4 temporal dynamics for texture encoding along perceptually defined axes.

Photorealistic Reconstruction of Visual Texture From EEG Signals.

Recent advances in brain decoding have made it possible to classify image categories based on neural activity. Increasing numbers of studies have further attempted to reconstruct the image itself. However, because images of objects and scenes inherently involve spatial layout information, the reconstruction usually requires retinotopically organized neural data with high spatial resolution, such as fMRI signals. In contrast, spatial layout does not matter in the perception of “texture,” which is known to be represented as spatially global image statistics in the visual cortex. This property of “texture” enables us to reconstruct the perceived image from EEG signals, which have a low spatial resolution. Here, we propose an MVAE-based approach for reconstructing texture images from visual evoked potentials measured from observers viewing natural textures such as the textures of various surfaces and object ensembles. This approach allowed us to reconstruct images that perceptually resemble the original textures with a photographic appearance. The present approach can be used as a method for decoding the highly detailed “impression” of sensory stimuli from brain activity.

Detection of Cotton Leaf Disease Using Image Processing Techniques

In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

Statistical Quantification of Texture Visual Features for Pattern Recognition by Analyzing Pre- and Post-Multispectral Landsat Satellite Imagery

This paper investigates the performance of visual texture features based on the gray-level co-occurrence matrix to analyze multispectral remotely sensed Landsat images. Different case studies related to urbanization, flood, and drought are investigated in this research work. The changing land use/land cover pattern caused by urbanization, floods, and droughts is examined through quantitative assessment. Texture visual features, i.e., correlation, contrast, angular second moment or energy, and homogeneity, are derived from the gray-level co-occurrence matrix. These features are used to develop a pattern for the changing texture of land use/land cover. Human visual perception of smoothness and coarseness is related to the texture features and is later used to describe the texture features’ changing behavior. The quantitative assessment of texture features in terms of smoothness and coarseness establishes a novel pattern between pre- and postimages of urbanization, flood, and drought.

Accurate physics-based digital reproduction of effect coatings.

We built an improved 3D rendering framework to accurately visualize the complete appearance of effect coatings, including metallic effects, sparkle and iridescence. Spectral reflectance measurements and sparkle indexes from a commercially available multi-angle spectrophotometer (BYKmac-i) were used together with physics-based approaches, such as flake-based reflectance models, to implement efficiently the appearance reproduction from a small number of bidirectional measurement geometries. With this rendering framework, we rendered a series of effect coating samples on an iPad display, simulating how these samples would be viewed inside a Byko-spectra effect light booth. We validated the appearance fidelity through psychophysical methods. We asked observers to evaluate the most important visual attributes that directly affect the appearance of effect coatings, i.e., the color, the angular dependence of color (color flop) and the visual texture (sparkle and graininess). Observers were asked to directly compare the rendered samples with the real samples inside the Byko-spectra effect light booth. In this study, we first validated the accuracy of rendering the color flop of effect coatings by conducting two separate visual tests, using flat and curved samples respectively. The results show an improved accuracy when curved samples were used (acceptability of 93% vs 80%). Secondly, we validated the digital reproduction of both color and texture by using a set of 30 metallic samples, and by including texture in the rendering using a sparkle model. We optimized the model parameters based on sparkle measurement data from the BYK-mac I instrument and using a matrix-adjustment model for optimization. The results from the visual tests show that the visual acceptability of the rendering is high at 90%.

RESTORATIVE EFFECT OF GREEN DESIGN OFFICE

In modern world, one of major causes of stress is reported at office. There have been many studies on the green effects in natural environment that improve human psychological and physiological states under stress situation. On the other hand, there is not enough space in the office for providing enough visual experiences of natural environment. Furthermore, there have been many discussions on the restorative effects of plants in the office environment based on subjective assessments but few research dealt with the physiological effect and they have not necessarily been successful in detecting clear effects of small interior plants. In this study, the authors ameliorate color and pattern of wall, floor, etc., which occupy major part of vision, of the conventional office with the plant's visual texture and conducted an experiment in which we aim at clarifying physiological restorative effects of our “green office scape” in virtual environment. The experiment consists of two Phases and 26 students participated in each phase in the same order. Each phase has two sessions of n-back tests and 30 seconds break between the sessions. Phase 1 started with the conventional office scape and it was changed to the “green office scape” at the beginning of break time, while in Phase 2, there was no change from the conventional office scape. During those phases, SCL values were measured and analyzed with the test scores as performance level. The whole experiment was conducted in virtual environment. As a result, at the break between the sessions, the average of standardized SCL values significantly increased (p < 0.05, n=22). However, the gap of these values is significantly higher when the scape changed from office to green than in no change condition (p < 0.05, n=22). In further analysis, the participants were classified into two groups who were aware of the change of the scape in Phase 1, i.e. “aware group (n=10)” and who were not, i.e. “unaware group (n=12)”. The results of the “aware group” showed that the average of standardized SCL values significantly increased when the design has changed into Green Design Office (p < 0.05) and the performance score kept a certain level, while in no change condition, the increase of SCL values was not significant and the performance score decreased significantly (p<0.01). From those results, the authors conclude that the Green Design Office may recover the worker's arousal level, and may bring the concentration. It indicates that continuous work in the same office design for a long time without the green elements may cause the difficulty in getting the restorative effect. In addition, the restorative effect by scape change into green ones may be effective when the worker is aware of the change. These results suggest that it is important to make the office workers aware of the green visual elements when changing office scape. Finally, those results imply that there is possibility of using virtual environment for the restoration of the worker's concentration.

Differential processing of reflection and rotation symmetries in visual textures

Differential processing of reflection and rotation symmetries in visual textures

Integration of visual and haptic texture information: Lederman &amp; Abbott revisited

Integration of visual and haptic texture information: Lederman &amp; Abbott revisited

Aynalı ve pullu sazan derisine uygulanan görüntü analizi ile görsel tekstür hesaplama yöntemlerinin karşılaştırılması

Regions of interest (ROI) representative of the visual texture of images of mirror carp Cyprinus carpio carpio and scaled carp Cyprinus carpio were taken. Red, green, blue and grayscale (R, G, B, GS) histograms of these ROI were calculated. The following methods of visual texture calculations were performed on the ROIs: 1) image energy based on histograms, 2) image entropy based on histograms, 3) image energy based on co-occurrence matrices, 4) image entropy based on co-occurrence matrices, 5) texture based on fractal dimensions, 6) texture based on texture primitives method. Calculations were performed for color and grayscale images. The identification of the smoothest and roughest ROIs depended on the method used. The largest range between the minimum and maximum values was found in the co-occurrence matrix-based entropy calculation. A close second was the texture change index (TCI) method.

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery.

This research work models two methods together to provide maximum information about a study area. The quantification of image texture is performed using the “grey level co-occurrence matrix (mathrm{GLCM})” technique. Image classification-based “object-based change detection (mathrm{OBCD})” methods are used to visually represent the developed transformation in the study area. Pre-COVID and post-COVID (during lockdown) panchromatic images of Connaught Place, New Delhi, are investigated in this research work to develop a model for the study area. Texture classification of the study area is performed based on visual texture features for eight distances and four orientations. Six different image classification methodologies are used for mapping the study area. These methodologies are “Parallelepiped classification (mathrm{PC}),” “Minimum distance classification (mathrm{MDC}),” “Maximum likelihood classification (mathrm{MLC}),” “Spectral angle mapper (mathrm{SAM}),” “Spectral information divergence (mathrm{SID})” and “Support vector machine (mathrm{SVM}).” mathrm{GLCM} calculations have provided a pattern in texture features contrast, correlation, mathrm{ASM}, and mathrm{IDM}. Maximum classification accuracy of 83.68% and 73.65% are obtained for pre-COVID and post-COVID image data through mathrm{MLC} classification technique. Finally, a model is presented to analyze before and after COVID images to get complete information about the study area numerically and visually.