Visual Appearance Research Articles

Influence of visual information on sniffing behavior in a routinely trichromatic primate.

Most catarrhine primates are considered to be strongly visually oriented, obtaining information about conspecifics and their environment from a diversity of visual cues. Other sensory modalities may provide information that is redundant and/or complimentary to visual cues. When cues from multiple sensory modalities are available, these may reinforce or suppress each other, as shown in several taxa ranging from insects to humans. Here, we tested how the presence and ambiguity of visual information affect the use of olfactory cues when exploring food and non-food items in semi-free-ranging Barbary macaques at Affenberg Salem, Germany. We presented monkeys with pipes containing food (peanuts, popcorn), non-food (stones, feces), or no items in transparent or opaque containers and assessed whether animals looked, sniffed, and/or grabbed into the pipes depending on the visibility of the contents (experiment 1). Visual information had no robust effect on sniffing probability, but monkeys were more likely to sniff before any other form of inspection if the can was opaque than if it was transparent. Both visual and olfactory information affected, whether or not monkeys attempted to retrieve the items from the pipes, whereby monkeys showed an overall decrease in the propensity to grab after sniffing. Furthermore, we manipulated the visual appearance of familiar food items (popcorn) with food colorant (experiment 2), which resulted in substantially increased olfactory inspections compared to unmanipulated popcorn. Taken together, reliance on the olfactory sense was modulated by the available visual information, emphasizing the interplay between different sensory modalities for obtaining information about the environment.

Effect of light emitting diodes (LEDs) irradiation on the functional quality and shelf life of basil microgreens

ABSTRACT Light-emitting diodes (LEDs) are the promising technology with low heat production and feasible spectra optimization, which improve plants’ function. In current study, basil microgreens as novel-leafy vegetables grown under LEDs (blue, red, 30%blue +70%red, and white) and sunlight to investigate phytochemical properties by comparing to sunlight-treated mature basil. Results showed that narrow wavelength including blue, red and blue/red LEDs had significant effect on increasing total phenol content (TPC) and antiradical activity (AA) of microgreens. Blue LED-treated microgreens exhibited significant increase in Potassium and the best visual appearance in comparison with mature and other microgreens. In the case of essential oils, methyl chavicol was the major compound in all samples with the main effect of broad light spectra; red LED- and blue LED-treated microgreens represented the maximum and minimum of methyl eugenol and β-caryophyllene, respectively, which could be related to the significant effect of red light on essential oil biosynthesis. No significant difference was observed in the market-acceptability of mature basil and microgreens which demonstrated that LEDs had no negative impact on organoleptic properties. Throughout the storage, darkness-stored microgreens showed the best result with the least weight loss and decrease in TPC; however, brightness-yellowness variation observed due to the plants’ aging.

Patterns in Icelandic Elf Hills

The elf dwellings of Icelandic rural storytelling culture and traditional folk belief appear to follow recurring, reasonably fixed patterns in their visual appearance, location, and ways in which local populations engage with them. This article presents a study of elf dwellings in the Strandir region of the Icelandic Westfjords, outlining these patterns through an in-depth engagement with a geographically limited but extensive dataset. The article shows how elf dwellings tend to be identified with erratic boulders and rocky hills of a particular size and appearance, generally located not in the ‘wilderness’, but close to places of everyday activity, especially near farmhouses, historic travel routes, and agricultural land. It highlights patterns of negative (avoidance) and positive (emotional attachment) engagement with such places. This makes a contribution not only to the study of traditional rural folk belief, but also to research discourses on the materiality of religion and spatiality of belief and storytelling.

The ‘sense of terroir’ among wine-consuming social media users in Italy

ABSTRACT The purpose of this study is to explore wine consumer appreciation towards terroir-based communication in the social media context. The Analytic Hierarchy Process (AHP) approach was applied to elicit consumer preferences for alternative Instagram posts including image-based and text-based signals about wine terroir. An online survey was administered to a sample of Italian wine consumers who use social media. Results showed that image-based posts were largely preferred to text-based posts. Among image posts, the visual appearance of the grape variety and the natural label, which means a combination of an intrinsic cue with an extrinsic one, gains higher user attention in comparison with the other signals. Among text posts, posts proposing a ‘terroir association’ received higher priority than those explaining a ‘terroir concept’. These results were tested analysing real Instagram posts of an Italian winery. The number of likes received by the different terroir-related posts reflected the AHP-estimated global priority weights highlighting that actual consumer appreciation agreed with the evaluation determined by using the proposed AHP model. The study revealed that the AHP was a useful approach to evaluate wine consumer engagement with digital communication. It contributed to understanding how wineries can effectively convey wine terroir meanings via social media.

Pigmental composition and physico-chemical characteristics of Bittergourd (Momordica charantia L. cv. Jadeite) during postharvest period as influenced by illumination colors

Fruits and vegetables that exhibit a higher chlorophyll content, as reflected in their visual appearance, are the preferred choice of consumers. The study aimed to evaluate the effects of Light Emitting Diodes (LEDs) on the physical, chemical, and pigmentation quality of bitter gourd using white, blue, and red at 1.5 W/135 lumens (Foshan Electrical and Lighting Co., Ltd [FSL], China). Bitter gourd, with a short postharvest life of 4-5 days due to physical and chemical disorders, was harvested weighing 300–400 g and 25 x 5 cm from the farm and subjected to varying illuminations within a 4-hour period for five days, with measurements taken daily. Statistical differences between treatments were observed in physicochemical parameters such as fruit shrivelling, yellowing, visual appearance, weight loss, dry matter content, total chlorophyll, pH values, and TA. The quality and shelf-life of bitter gourd fruits were found to be improved by the white LED. The visual appearance was maintained, and fruit shrivelling and yellowing were delayed, with lower weight loss observed. Slight changes in chlorophylls and carotenoids, vitamin C, and a shelf-life of 5 days were recorded.

VISAtlas: An Image-Based Exploration and Query System for Large Visualization Collections via Neural Image Embedding.

High-quality visualization collections are beneficial for a variety of applications including visualization reference and data-driven visualization design. The visualization community has created many visualization collections, and developed interactive exploration systems for the collections. However, the systems are mainly based on extrinsic attributes like authors and publication years, whilst neglect intrinsic property (i.e., visual appearance) of visualizations, hindering visual comparison and query of visualization designs. This paper presents VISAtlas, an image-based approach empowered by neural image embedding, to facilitate exploration and query for visualization collections. To improve embedding accuracy, we create a comprehensive collection of synthetic and real-world visualizations, and use it to train a convolutional neural network (CNN) model with a triplet loss for taxonomical classification of visualizations. Next, we design a coordinated multiple view (CMV) system that enables multi-perspective exploration and design retrieval based on visualization embeddings. Specifically, we design a novel embedding overview that leverages contextual layout framework to preserve the context of the embedding vectors with the associated visualization taxonomies, and density plot and sampling techniques to address the overdrawing problem. We demonstrate in three case studies and one user study the effectiveness of VISAtlas in supporting comparative analysis of visualization collections, exploration of composite visualizations, and image-based retrieval of visualization designs. The studies reveal that real-world visualization collections (e.g., Beagle and VIS30K) better accord with the richness and diversity of visualization designs than synthetic collections (e.g., Data2Vis), inspiring composite visualizations are identified in real-world collections, and distinct design patterns exist in visualizations from different sources.

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint.

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Estrategias y directrices para la configuración estética del QR en publicidad

QR codes are sometimes used as a vital component of digital marketing strategies that seek to attract consumers' attention by converting their dull, black-and-white, checkered appearance into attractive designs. However, as changes to their visual appearance can lead to scanning failures, an understanding of the technical requirements and limitations of this technology is essential in order to develop a viable creative strategy. To this end, a semi-systematic review of the literature was carried out for the purpose of gathering information on possible strategies for enhancing QR codes without compromising their functionality, and to provide guidelines for manually customising these codes in a way that is effective. The aim of this paper is to reveal the amount of research that has focused on the relationship between the aesthetic characteristics of QR codes and their advertising effectiveness. The main findings include a scarcity of scientific analysis regarding this issue, yet multiple opportunities for the artistic modification of QR codes due to recent advances in generative artificial intelligence.

Screening Subtropical Lettuce Accessions for Heat Tolerance by Incorporating Accelerated Shelf Life Testing

Lettuce (Lactuca sativa L.) is grown worldwide, from temperate to subtropical climates. Spring season production in humid, subtropical regions, such as southern Florida, is characterized by rising ambient temperatures that can stress lettuce to prematurely bolt and lose shelf life. The objectives of this research were 1) to identify genetic variability in heat tolerance and shelf life among lettuce types and accessions grown under humid, subtropical conditions, and 2) to understand the genotype × environment (G × E) interaction to estimate shelf life of these lettuce accessions. Five lettuce types (romaine, crisphead, butterhead, leaf, and Latin) were grown under commercial conditions in the Everglades Agricultural Area near Belle Glade, FL, USA, for five field experiments over two seasons. Lettuce heads were evaluated at harvest, and subsets were transported to a local commercial grower/shipper for vacuum-cooling and storage at 15 °C according to previously determined protocols for accelerated shelf life testing. Visual appearance ratings were made across harvests and storage time points to segregate lettuce accessions with an estimated marketable shelf life >14 days. The breeding lines tested in this research had head weight and marketability comparable to commercial cultivars. Notably, the crisphead accessions 50113, 60157, 60159, and H1098 had the highest estimated and actual shelf life of more than 21 days, with no presence of bolting or tipburn. Meanwhile, romaine, butterhead, leaf, and Latin types had accessions with estimated and actual shelf life ranging from 14 to 28 days and no presence of bolting or tipburn. A G × E analysis indicated that this interaction is significant; therefore, breeders should consider analyzing G × E when developing new cultivars with good horticultural characteristics, longer shelf life, and most importantly, adaptation to warmer humid, subtropical conditions.

A deep learning approach to detection of oral cancer lesions from intra oral patient images: A preliminary retrospective study

IntroductionOral squamous cell carcinomas (OSCC) seen in the oral cavity are a category of diseases for which dentists may diagnose and even cure. This study evaluated the performance of diagnostic computer software developed to detect oral cancer lesions in intra-oral retrospective patient images. Materials and methodsOral cancer lesions were labeled with CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) and polygonal type labeling method on a total of 65 anonymous retrospective intraoral patient images of oral mucosa that were diagnosed with oral cancer histopathologically by incisional biopsy from individuals in our clinic. All images have been rechecked and verified by experienced experts. This data set was divided into training (n = 53), validation (n = 6) and test (n = 6) sets. Artificial intelligence model was developed using YOLOv5 architecture, which is a deep learning approach. Model success was evaluated with confusion matrix. ResultsWhen the success rate in estimating the images reserved for the test not used in education was evaluated, the F1, sensitivity and precision results of the artificial intelligence model obtained using the YOLOv5 architecture were found to be 0.667, 0.667 and 0.667, respectively. ConclusionsOur study reveals that OCSCC lesions carry discriminative visual appearances, which can be identified by deep learning algorithm. Artificial intelligence shows promise in the prediagnosis of oral cancer lesions. The success rates will increase in the training models of the data set that will be formed with more images.

PMSGAN: Parallel Multistage GANs for Face Image Translation.

In this article, we address the face image translation task, which aims to translate a face image of a source domain to a target domain. Although significant progress has been made by recent studies, face image translation is still a challenging task because it has more strict requirements for texture details: even a few artifacts will greatly affect the impression of generated face images. Targeting to synthesize high-quality face images with admirable visual appearance, we revisit the coarse-to-fine strategy and propose a novel p arallel m ultistage architecture on the basis of g enerative a dversarial n etworks (PMSGAN). More specifically, PMSGAN progressively learns the translation function by disintegrating the general synthesis process into multiple parallel stages that take images with gradually decreasing spatial resolution as inputs. To prompt the information exchange between various stages, a cross-stage atrous spatial pyramid (CSASP) structure is specially designed to receive and fuse the contextual information from other stages. At the end of the parallel model, we introduce a novel attention-based module that leverages multistage decoded outputs as in situ supervised attention to refine the final activations and yield the target image. Extensive experiments on several face image translation benchmarks show that PMSGAN performs considerably better than state-of-the-art approaches.

Investigating nuisance dust complaints: Combining high frequency dust deposition records and source identification using integrated microanalytical techniques

While ducted dust emissions from industry are usually well regulated and controlled, fugitive dust emissions can be difficult to quantify or differentiate from off-site emissions, particularly where fugitive emissions are transient or intermittent. In this study, sources of nuisance dust were investigated using a novel dust deposition sampling methodology, followed by chemical and mineralogical analysis using optical microscopy, x-ray diffraction (XRD), scanning electron microscopy with energy dispersive x-ray spectrometry (SEM-EDS), and isotope ratio mass spectrometry analysis. Dust deposition over 24 h was collected on horizontally orientated 300 mm square glass panels at 6 sites in the region of interest. This method captured very small (<10 mg) masses of dust and could distinguish very small variations in dust deposition at different sites and on different days. Comparison samples collected from potential dust sources at a nearby lime manufacturing facility, and other potential fugitive dust sources in the region were differentiated using XRD mineralogy, visual appearance by optical microscopy and SEM-EDS analysis of individual particles. Comparison of selected dust samples to reference samples indicated most dust deposition at the study sites consisted of soil or sand. However, dust deposition at two sites was sometimes composed of a material that was similar in composition to lime that had been rehydrated to portlandite, but not yet fully carbonated to calcite. Subsequent analysis of δ13C and δ18O for selected dust samples from these two sites were consistent with rapid and recent carbonation of small particles in an alkaline environment, which could have occurred during the atmospheric release and transport of lime, and ruled out fugitive dust emission from lime stockpiles existing on site. Combining the information obtained from high frequency record of dust deposition with targeted mineralogical, isotopic and morphological examinations provided new insight into the source of fugitive dust emissions in this area.

Usability Media Pembelajaran Geometri Berbasis GeoGebra untuk Meningkatkan Kemampuan Numerasi Mahasiswa

The problem in this study is how feasible the GeoGebra-based learning media developed to enhance students' numeracy ability . The objective of this research is to identify the aspects of GeoGebra-based geometry learning media that need improvement. The research employs a mixed-methods approach. The conclusion of this study is that the development of GeoGebra-based learning media should focus on improving accuracy and completeness of the material, alignment with the curriculum, relevance of examples and exercises, visual appearance, interaction, user-friendliness, and adequate technical support. There is a need for more precise, accurate, and comprehensive material presentation. Learning media should ensure that all topics are thoroughly covered according to the applicable curriculum. The content needs to be more aligned with current educational standards to ensure relevance and appropriateness. Examples of mathematics applications in daily life and interactive exercises can enhance students' understanding. The presentation structure should be more logical and organized to facilitate navigation and use by students. The use of interactive tools such as animations and quizzes can help users understand the material. Access time to the media should be accelerated, and visual appeal should be enhanced to attract students' interest in learning. Quick guides and brief tutorials should be available to assist new users.

Application of active and environment-friendly poly (3-hydroxybutyrate)/grapeseed oil/MgO nanocomposite packaging for prolonging the shelf-life of cherry tomatoes (Solanum lycopersicum L. var.cerasiforme)

Active packaging is one of the emerging technologies for maintaining the safety and quality of fresh produce. This work assessed the effectiveness of an active and eco-friendly poly (3-hydroxybutyrate) (PHB) based film loaded with 5 wt% grapeseed oil (GS) and 0.7 wt% MgO nanoparticles (MgO NPs) prepared via sonication-assisted solution casting route for storage and handling of the cherry tomatoes. The shelf-life study of cherry tomatoes in active (PHB/5GS/0.7MgO) packaging was carried out for 23 days in ambient room conditions, and results are compared with the storage of cherry tomatoes without packaging (control), in low-density polyethylene (LDPE) packaging, and in PHB packaging. Various parameters, viz. Visual appearance, weight loss, firmness, total soluble solids (TSS), titratable acidity (TA), ripening index, pH, color, and organoleptic factors, were evaluated every 3 days for cherry tomatoes in various storage groups. The results from the shelf-life study clearly showed that the cherry tomatoes stored in PHB/5GS/0.7MgO packaging experienced better preservation of quality than those in other storage groups, with no signs of surface wrinkling, shrinkage, microbial growth, or juice leakage till day 21 of their storage. This was further evidenced by minimal weight loss (4.45 ± 0.06% on day 21), minimal change in titratable acidity (0.74 ± 0.01% on day 0–0.67 ± 0.01% on day 21), and better retention of firmness (19.89 ± 0.12 N on day 0–15.77 ± 0.37 N on day 21), pH, color, and organoleptic attributes for the cherry tomatoes stored in this packaging group. Furthermore, it was found that the migration of MgO NPs from the nanocomposite packaging to cherry tomatoes was negligible. These findings suggest that the PHB/5GS/0.7MgO nanocomposite film is a promising choice for the storage of cherry tomatoes.

UPGAN: An Unsupervised Generative Adversarial Network Based on U-Shaped Structure for Pansharpening

Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and achieve excellent image quality; however, the images generated with supervised learning approaches lack real-world applicability. Therefore, in this study, we propose an unsupervised pansharpening method based on a generative adversarial network. Considering the fine tubular structures in remote sensing images, a dense connection attention module is designed based on dynamic snake convolution to recover the details of spatial information. In the stage of image fusion, the fusion of features in groups is applied through the cross-scale attention fusion module. Moreover, skip layers are implemented at different scales to integrate significant information, thus improving the objective index values and visual appearance. The loss function contains four constraints, allowing the model to be effectively trained without reference images. The experimental results demonstrate that the proposed method outperforms other widely accepted state-of-the-art methods on the QuickBird and WorldView2 data sets.

GOYA: Leveraging Generative Art for Content-Style Disentanglement.

The content-style duality is a fundamental element in art. These two dimensions can be easily differentiated by humans: content refers to the objects and concepts in an artwork, and style to the way it looks. Yet, we have not found a way to fully capture this duality with visual representations. While style transfer captures the visual appearance of a single artwork, it fails to generalize to larger sets. Similarly, supervised classification-based methods are impractical since the perception of style lies on a spectrum and not on categorical labels. We thus present GOYA, which captures the artistic knowledge of a cutting-edge generative model for disentangling content and style in art. Experiments show that GOYA explicitly learns to represent the two artistic dimensions (content and style) of the original artistic image, paving the way for leveraging generative models in art analysis.

Shell stand: Stable thin shell models for 3D fabrication

A thin shell model refers to a surface or structure, where the object’s thickness is considered negligible. In the context of 3D printing, thin shell models are characterized by having lightweight, hollow structures, and reduced material usage. Their versatility and visual appeal make them popular in various fields, such as cloth simulation, character skinning, and for thin-walled structures like leaves, paper, or metal sheets. Nevertheless, optimization of thin shell models without external support remains a challenge due to their minimal interior operational space. For the same reasons, hollowing methods are also unsuitable for this task. In fact, thin shell modulation methods are required to preserve the visual appearance of a two-sided surface which further constrain the problem space. In this paper, we introduce a new visual disparity metric tailored for shell models, integrating local details and global shape attributes in terms of visual perception. Our method modulates thin shell models using global deformations and local thickening while accounting for visual saliency, stability, and structural integrity. Thereby, thin shell models such as bas-reliefs, hollow shapes, and cloth can be stabilized to stand in arbitrary orientations, making them ideal for 3D printing.

A multifactorial screening model based on the Graves ophthalmopathy quality of life scores in dysthyroid optic neuropathy.

To validate the Graves ophthalmopathy quality of life (GO-QOL) questionnaire in screening DON and to construct an effective model. A total of 194 GO patients were recruited and divided into DON and non-DON (mild and moderate-to-severe) groups. Eye examinations were performed, and quality of life was assessed by the GO-QOL questionnaire. The random forest, decision tree model, receiver operator characteristic (ROC) curve, accuracy and Brier score were determined by R software. In GO-QOL, age, best corrected visual acuity (BCVA), exophthalmos, CAS, severity, and Gorman score were found to be factors related to visual function scores. On the appearance scale, gender, duration of GO, BCVA, exophthalmos, CAS and severity of GO were relevant. Both the visual function scores and appearance scores were significantly lower in DON groups than in non-DON groups (33.18 ± 24.54 versus 81.26 ± 17.39, 60.08 ± 24.82 versus 76.14 ± 27.56). The sensitivity, specificity, and AUC of the visual function scores were 91.1%, 81.7% and 0.939, respectively Visual function scores were used to construct a decision tree model. The sensitivity, specificity, and AUC of the model were 92.9%, 88.0% and 0.941, respectively, with an accuracy of 89.7% and a Brier score of 0.024. Visual function scores were qualified as a screening method for DON, with a cutoff point of 58. A multifactorial screening model based on visual function scores was constructed.

Rozpoznawanie materiałów budowlanych i ekstrakcja cech przy użyciu małego czujnika CCD oraz technik analizy obrazu i klastrowania

Heritage Building material recognition is the process of classifying building materials based on their visual appearance. It is important in construction, urban planning, and archaeology. Image analysis is a common approach, starting with acquiring RGB images, then extracting features using techniques such as colour histograms and texture analysis, and clustering the materials into groups using algorithms like k-means. Finally, the materials are classified into categories using classifiers like decision trees, SVM, or neural networks. Image analysis is a useful tool for building material recognition, as it allows for accurate classification of building materials based on their visual characteristics.

Towards Decreasing Encroachments on Conserved Historic Buildings, Users Participation as an Approach: The Case of Youth Center- Sebastia

This paper examines what, why, and how users’ behavior can cause encroachments on historic buildings after conservation. It also introduces users’ participation in the conservation process as an approach to containing these encroachments. With this aim, this paper reviews the literature and extracts three approaches to user participation in the conservation process, which are social-benefits, economic-benefits, as well as emotional-needs. To examine the possible role of each approach in containing the negative practices by users, the youth center in Sebastia, Palestine has been analyzed as a case study in which the process of the encroachment of the users of these types of buildings and the reasons behind them have been studied. In addition to the former, this paper presents the users’ role in the deterioration of historic value and the visual appearance of the building within the short period of conservation. Based on this analysis, it was found that the encroachments have manifested in different forms, such as unsystematic interventions, sporadic neglect, and vandalism. Such intrusions on these structures are a direct result of not involving users in the conservation process. Thus, the paper discusses involving users in the former process to limit harmful practices using suitable approaches including users.