Specific Visual Features Research Articles

The vision of haptics tunes the somatosensory threshold

The interaction between different sensory modalities represents a crucial issue in the neuroscience of consciousness: when the processing of one modality is deficient, the concomitant presentation of stimuli of other spared modalities may sustain the restoration of the damaged sensory functions. In this regard, visual enhancement of touch may represent a viable tool in rehabilitating tactile disorders, yet the specific visual features mostly modulating the somatosensory experience remain unsettled.In this study, healthy subjects underwent a tactile detection task during the observation of videos displaying different contents, including static gratings, meaningless motions and natural or point-lights reach-to-grasp-and-manipulate actions. Concurrently, near-threshold stimuli were delivered to the median nerve at different time-points. The subjective report was collected after each trial; the sensory detection rate was computed and compared across video conditions.Our results indicate that the specific presence of haptic contents (i.e., the vision of manipulation), either fully displayed or implied by point-lights, magnifies tactile sensitivity. The notion that such stimuli prompt a conscious tactile experience opens to novel rehabilitation approaches for tactile consciousness disorders.

Color‐nutrient associations: Implications for product design of dietary supplements

AbstractDietary supplements are prevalent and represent a huge (and still growing) global market. Such products are intended to enhance health and wellness as well as to supplement diets. Compared to the large amount of dietary supplements research on nutrition, dietetics, and medicine, far less research has been conducted in the realm of sensory and consumer science. Product design is undoubtedly an important element of any brand's success, no matter what the category, and can be used to convey various symbolic meanings to the consumer. Drawing on research on color psychology and product design, the present research aimed to clarify the associations (or expectations) that consumers have with the particular product design of dietary supplements. Across four studies, two of which were preregistered, participants were asked to choose a color that they felt best matched each of the named nutrients. The results consistently demonstrate that people associate nutrients with particular colors (i.e., color patches; Study 1) and to capsule/container color; Studies 2–4. Vitamins supplements in general, as well as vitamins C and D, in particular, were frequently matched with the colors yellow and orange. Dietary fiber supplement was often matched with green. Although mineral supplements were frequently matched with blue, each kind of mineral appeared to have its own unique association with specific colors (magnesium/iron with gray, calcium with white). Protein supplements (protein, amino acid) are frequently matched with the colors orange and red. Taken together, these findings demonstrate how people associate nutrients with specific visual design features and thus contribute practical knowledge concerning the design of dietary supplements.Practical ApplicationsOur findings provide practical implications for effective communication strategies for nutrition with the consumer. Color is an important design element, and the design of nutritional supplements and their packaging is no exception in this regard. Designers and marketers can use our findings to help create better visual designs for dietary supplements and their packaging to help meet consumers' expectations. Moreover, our findings might also be fruitfully applied to marketing communications for functional foods (e.g., vitamin‐enriched foods, mineral‐enriched foods). Marketers may be able to capitalize on our findings concerning color‐nutrition associations in order to effectively promote functional foods.

Detection of Covid-19 Using CAD System Depending on Chest X-Ray and Machine Learning Techniques

SARS-COV-2 (severe acute respiratory syndrome coronavirus-2) has caused widespread mortality. Infected individuals had specific radiographic visual features and fever, dry cough, lethargy, dyspnea, and other symptoms. According to the study, the chest X-ray (CXR) is one of the essential non-invasive clinical adjuncts for detecting such visual reactions associated with SARS-COV-2. Manual diagnosis is hindered by a lack of radiologists’ availability to interpret CXR images and by the faint appearance of illness radiographic responses. The paper describes an automatic COVID detection based on the deep learning-based system that applied transfer learning techniques to extract features from CXR images to distinguish. The system has three main components. The first part is extracting CXR features with MobileNetV2. The second part used the extracted features and applied Dimensionality reduction using LDA. The final part is a Classifier, which employed XGBoost to classify dataset images into Normal, Pneumonia, and Covid-19. The proposed system achieved both immediate and high results with an overall accuracy of 0.96%, precision of 0.95%, recall of 0.94%, and F1 score of 0.94%.

Impact of Visual Game-Like Features on Cognitive Performance in a Virtual Reality Working Memory Task: Within-Subjects Experiment

BackgroundAlthough the pursuit of improved cognitive function through working memory training has been the subject of decades of research, the recent growth in commercial adaptations of classic working memory tasks in the form of gamified apps warrants additional scrutiny. In particular, the emergence of virtual reality as a platform for cognitive training presents opportunities for the use of novel visual features.ObjectiveThis study aimed to add to the body of knowledge regarding the use of game-like visual design elements by specifically examining the application of two particular visual features common to virtual reality environments: immersive, colorful backgrounds and the use of 3D depth. In addition, electroencephalography (EEG) data were collected to identify potential neural correlates of any observed changes in performance.MethodsA simple visual working memory task was presented to participants in several game-like adaptations, including the use of colorful, immersive backgrounds and 3D depth. The impact of each adaptation was separately assessed using both EEG and performance assessment outcomes and compared with an unmodified version of the task.ResultsResults suggest that although accuracy and reaction time may be slightly affected by the introduction of such game elements, the effects were small and not statistically significant. Changes in EEG power, particularly in the beta and theta rhythms, were significant but failed to correlate with any corresponding changes in performance. Therefore, they may only reflect cognitive changes at the perceptual level.ConclusionsOverall, the data suggest that the addition of these specific visual features to simple cognitive tasks does not appear to significantly affect performance or task-dependent cognitive load.

315 Development of a Mobile Software Application to Promote Education, Self-Care, and Treatment Follow-Up among Puerto Rican Breast Cancer Patients

OBJECTIVES/GOALS: This project will design, develop and pilot-test a Spanish-mobile application for breast cancer patients. It is intended that patients will improve treatment adherence, have a personal information storage system for their follow-up healthcare and can have greater control in the processes of diagnosis, treatment, education, and recovery. METHODS/STUDY POPULATION: In the first phase, a team of undergraduate engineering students was recruited to work on the areas of: Front End, Database Development, Security Protocols, and Integration. Team Venus will provide the cancer-related content. In the second phase, the engineering team will identify bugs, usability, satisfaction, and workability of the application among a pilot convenience sample of 20 participants from four cancer clinics and the community. The inclusion criteria are being > 21 years old and having a cell phone. The exclusion criterion is not knowing how/being able to use a mobile application. Participants will complete a questionnaire on sociodemographic data, mobile phone and application use, efficacy, usability, and security. Univariate and bivariate statistical analysis will be performed using SPSS. RESULTS/ANTICIPATED RESULTS: The researchers are defining specific visual and content features they would like the application to have. They are also searching and collecting reliable information about cancer from primary sources to incorporate into the tools available to the application’s users. In addition, they have also begun to identify potential cancer treatment facilities and medical personnel to assist in the recruitment of patients for pilot-testing the application. The researchers have divided them into geographical areas in Puerto Rico and each Team Venus member will approach and orient patients and medical personnel about the project. DISCUSSION/SIGNIFICANCE: It is intended that patients will improve treatment adherence, have a storage system for their follow-up healthcare and greater control in their processes of diagnosis, treatment, education, and recovery. Future goals include use of the database feature by researchers and expanding the testing of the application with a larger patient-base.

Search image formation for spider prey in a mud dauber wasp.

Search images are perceptual biases acquired through experience that improve an individual’s ability to detect the object of their search (e.g., a predator seeking prey). In hymenopterans, examples include floral search images in bees and acquired sensory biases towards specific prey in wasp predators. Mud dauber wasps exhibit individual specialization and consistency in prey preferences through time, often based on visual and chemical cues, that may vary at intraspecific levels. However, the search image concept in wasps has not been evaluated from a visual ecology perspective. Using psychophysical visual modelling from multispectral digital photography, we measured the colour and body pattern of the spiders captured and stored in nests by the wasp Trypoxylon mexicanum. Those visual traits were compared at the community and nest cell levels, according to the nest provisioning sequence, and its relationship with spider diversity. Our results show that the wasps are choosing a small group of orb weavers (Araneidae) with similar coloration and body patterns. These findings highlight the specific visual features that may be the focal trait during search image formation in these wasps. The colour and pattern variables of spider prey were consistent over time, suggesting that individual search images may strengthen with experience.

Transience of the Retinal Output Is Determined by a Great Variety of Circuit Elements.

Retinal ganglion cells (RGCs) encrypt stimulus features of the visual scene in action potentials and convey them toward higher visual centers in the brain. Although there are many visual features to encode, our recent understanding is that the ~46 different functional subtypes of RGCs in the retina share this task. In this scheme, each RGC subtype establishes a separate, parallel signaling route for a specific visual feature (e.g., contrast, the direction of motion, luminosity), through which information is conveyed. The efficiency of encoding depends on several factors, including signal strength, adaptational levels, and the actual efficacy of the underlying retinal microcircuits. Upon collecting inputs across their respective receptive field, RGCs perform further analysis (e.g., summation, subtraction, weighting) before they generate the final output spike train, which itself is characterized by multiple different features, such as the number of spikes, the inter-spike intervals, response delay, and the rundown time (transience) of the response. These specific kinetic features are essential for target postsynaptic neurons in the brain in order to effectively decode and interpret signals, thereby forming visual perception. We review recent knowledge regarding circuit elements of the mammalian retina that participate in shaping RGC response transience for optimal visual signaling.

Generalized Scene Classification From Small-Scale Datasets With Multitask Learning

Remote sensing images contain a wealth of spatial information. Efficient scene classification is a necessary precedent step for further application. Despite the great practical value, the mainstream methods using deep convolutional neural networks (CNNs) are generally pretrained on other large datasets (such as ImageNet) and thus fail to capture the specific visual characteristics of remote sensing images. For another, it lacks the generalization ability to new tasks when training a new CNN from scratch with an existing remote sensing dataset. This article addresses the dilemma and uses multiple small-scale datasets to learn a generalized model for efficient scene classification. Since the existing datasets are heterogeneous and cannot be directly combined to train a network, a multitask learning network (MTLN) is developed. The MTLN treats each small-scale dataset as an individual task and uses complementary information contained in multiple tasks to improve generalization. Concretely, the MTLN consists of a shared branch for all tasks and multiple task-specific branches with each for one task. The shared branch extracts shared features for all tasks to achieve information sharing among tasks. The task-specific branch distills the shared features into task-specific features toward the optimal estimation of each specific task. By jointly learning shared features and task-specific features, the MTLN maintains both generalization and discrimination abilities. Two types of MTL scenarios are explored to validate the effectiveness of the proposed method: one is to complete multiple scene classification tasks and the other is to jointly perform scene classification and semantic segmentation.

ENHANCED PERIPHERAL FACE PROCESSING IN DEAF INDIVIDUALS.

Studies of compensatory changes in visual functions in response to auditory loss have shown that enhancements tend to be restricted to the processing of specific visual features, such as motion in the periphery. Previous studies have also shown that deaf individuals can show greater face processing abilities in the central visual field. Enhancements in the processing of peripheral stimuli are thought to arise from a lack of auditory input and a subsequent increase in the allocation of attentional resources to peripheral locations, while enhancements in face processing abilities are thought to be driven by experience with ASL and not necessarily hearing loss. This combined with the fact that face processing abilities typically decline with eccentricity suggests that face processing enhancements may not extend to the periphery for deaf individuals. Using a face matching task, we examined whether deaf individuals' enhanced ability to discriminate between faces extends to the peripheral visual field. Deaf participants were more accurate than hearing participants in discriminating faces presented both centrally and in the periphery. Our results support earlier findings that deaf individuals possess enhanced face discrimination abilities in the central visual field and further extend them by showing that these enhancements also occur in the periphery for more complex stimuli.

Stylistics Analysis Of National Geographic Magazine’s Cover June Edition

This research presented the Stylistics analysis of National Geographic magazine’s cover June edition. The magazine was chosen because it is one of the magazine's newest issues by the time the researcher conducting this research. The Stylistic approach was employed to understand and reveal the magazine editor's purposes in using specific language and visual features. Considering this research analyzes visual feature; therefore, the theory of visual grammar was employed. To achieve the analysis's goal, the researcher analyzed the language features from three language levels and a feature from visual grammar. In the level of phonology, the features are assonance, alliteration, and consonance. In the level of graphology, the features are italicization, uppercase, and lowercase. In the level of typography, the features are font size and the position of the text. In the visual grammar, the feature is distance. The researcher found that the language and visual features are used in purposes. They were used to give some effects to the reader that can attract them to read the magazine. In conclusion, the stylistics was proven in revealing the editor’s purposes of using language and visual feature in the magazine cover. Keywords: stylistics, visual analysis, magazine cover

Learning to perceive shape from temporal integration following late emergence from blindness

Visual perception requires massive use of inference because the 3D structure of the world is not directly provided by the sensory input.1 Particularly challenging is anorthoscopic vision-when an object moves behind a narrow slit such that only a tiny fraction of it is visible at any instant. Impressively, human observers correctly recognize objects in slit-viewing conditions by early childhood,2,3 via temporal integration of the contours available in each sliver.4,5 But can this capability be acquired if one has been effectively blind throughout childhood? We studied 23 Ethiopian children which had bilateral early-onset cataracts-resulting in extremely poor vision in infancy-and surgically treated only years later. We tested their anorthoscopic vision, precisely because it requires a cascade of demanding visual inference processes to perceive veridical shape. Failure to perform the task may allow mapping specific bottlenecks for late visual recovery. The patients' visual acuity typically improved substantially within 6months post-surgery. Still, at this stage many were unable to recover shape under slit-viewing conditions, although they could infer the direction of global motion. However, when retested later, almost all patients could judge shape in slit-conditions necessitating temporal integration. This acquired capability often transferred to novel stimuli, in similar slit-viewing conditions. Thus, learning was not limited to the specific visual features of the original shapes. These results indicate that plasticity of sophisticated visual inference routines is preserved well into adolescence, and vision restoration after prolonged early-onset blindness is feasible to a greater extent than previously thought.

Nonlinear spatial integration in retinal bipolar cells shapes the encoding of artificial and natural stimuli

SummaryThe retina dissects the visual scene into parallel information channels, which extract specific visual features through nonlinear processing. The first nonlinear stage is typically considered to occur at the output of bipolar cells, resulting from nonlinear transmitter release from synaptic terminals. In contrast, we show here that bipolar cells themselves can act as nonlinear processing elements at the level of their somatic membrane potential. Intracellular recordings from bipolar cells in the salamander retina revealed frequent nonlinear integration of visual signals within bipolar cell receptive field centers, affecting the encoding of artificial and natural stimuli. These nonlinearities provide sensitivity to spatial structure below the scale of bipolar cell receptive fields in both bipolar and downstream ganglion cells and appear to arise at the excitatory input into bipolar cells. Thus, our data suggest that nonlinear signal pooling starts earlier than previously thought: that is, at the input stage of bipolar cells.

Goodpoint: unsupervised learning of key point detection and description

Subject of Research. The paper presents the study of algorithms for key point detection and description, widely used in computer vision. Typically, the corner detector acts as a key point detector, including neural key point detectors. For some types of images obtained in medicine, the application of such detectors is problematic due to the small number of detected key points. The paper considers a problem of a neural network key point detector training on unlabeled images. Method. We proposed the definition of key points not depending on specific visual features. A method was considered for training of a neural network model meant for detecting and describing key points on unlabeled data. The application of homographic image transformation was basic to the method. The neural network model was trained to detect the same key points on pairs of noisy images related to a homographic transformation. Only positive examples were used for detector training, just points correctly matched with features produced by the neural network model for key point description. Main Results. The unsupervised learning algorithm is used to train the neural network model. For the ease of comparison, the proposed model has a similar architecture and the same number of parameters as the supervised model. Model evaluation is performed on the three different datasets: natural images, synthetic images, and retinal photographs. The proposed model shows similar results to the supervised model on the natural images and better results on retinal photographs. Improvement of results is demonstrated after additional training of the proposed model on images from the target domain. This is an advantage over a model trained on a labeled dataset. For comparison, the harmonic average of such metrics is used as: the accuracy and the depth of matching by descriptors, reproducibility of key points and image coverage. Practical Relevance. The proposed algorithm makes it possible to train the neural network key point detector together with the feature extraction model on images from the target domain without costly dataset labeling and reduce labor costs for the development of the system that uses the detector.

Global-Affine and Local-Specific Generative Adversarial Network for semantic-guided image generation

<p style='text-indent:20px;'>The recent progress in learning image feature representations has opened the way for tasks such as label-to-image or text-to-image synthesis. However, one particular challenge widely observed in existing methods is the difficulty of synthesizing fine-grained textures and small-scale instances. In this paper, we propose a novel Global-Affine and Local-Specific Generative Adversarial Network (GALS-GAN) to explicitly construct global semantic layouts and learn distinct instance-level features. To achieve this, we adopt the graph convolutional network to calculate the instance locations and spatial relationships from scene graphs, which allows our model to obtain the high-fidelity semantic layouts. Also, a local-specific generator, where we introduce the feature filtering mechanism to separately learn semantic maps for different categories, is utilized to disentangle and generate specific visual features. Moreover, we especially apply a weight map predictor to better combine the global and local pathways considering the highly complementary between these two generation sub-networks. Extensive experiments on the COCO-Stuff and Visual Genome datasets demonstrate the superior generation performance of our model against previous methods, our approach is more capable of capturing photo-realistic local characteristics and rendering small-sized entities with more details.</p>

Enhanced Peripheral Face Processing in Deaf Individuals †

Studies of compensatory changes in visual functions in response to auditory loss have shown that enhancements tend to be restricted to the processing of specific visual features, such as motion in the periphery. Previous studies have also shown that deaf individuals can show greater face processing abilities in the central visual field. Enhancements in the processing of peripheral stimuli are thought to arise from a lack of auditory input and subsequent increase in the allocation of attentional resources to peripheral locations, while enhancements in face processing abilities are thought to be driven by experience with American sign language and not necessarily hearing loss. This combined with the fact that face processing abilities typically decline with eccentricity suggests that face processing enhancements may not extend to the periphery for deaf individuals. Using a face matching task, the authors examined whether deaf individuals’ enhanced ability to discriminate between faces extends to the peripheral visual field. Deaf participants were more accurate than hearing participants in discriminating faces presented both centrally and in the periphery. Their results support earlier findings that deaf individuals possess enhanced face discrimination abilities in the central visual field and further extend them by showing that these enhancements also occur in the periphery for more complex stimuli.

Functional, molecular and morphological heterogeneity of superficial interneurons in the larval zebrafish tectum.

The superficial interneurons, SINs, of the zebrafish tectum, have been implicated in a range of visual functions, including size discrimination, directional selectivity, and looming-evoked escape. This raises the question if SIN subpopulations, despite their morphological similarities and shared anatomical position in the retinotectal processing stream, carry out diverse, task-specific functions in visual processing, or if they have simple tuning properties in common. Here we have further characterized the SINs through functional imaging, electrophysiological recordings, and neurotransmitter typing in two transgenic lines, the widely used Gal4s1156t and the recently reported LCRRH2-RH2-2:GFP. We found that about a third of the SINs strongly responded to changes in whole-field light levels, with a strong preference for OFF over ON stimuli. Interestingly, individual SINs were selectively tuned to a diverse range of narrow luminance decrements. Overall responses to whole-field luminance steps did not vary with the position of the SIN cell body along the depth of the tectal neuropil or with the orientation of its neurites. We ruled out the possibility that intrinsic photosensitivity of Gal4s1156t+ SINs contribute to the measured visual responses. We found that, while most SINs express GABAergic markers, a substantial minority express an excitatory neuronal marker, the vesicular glutamate transporter, expanding the possible roles of SIN function in the tectal circuitry. In conclusion, SINs represent a molecularly, morphologically, and functionally heterogeneous class of interneurons, with subpopulations that detect a range of specific visual features, to which we have now added narrow luminance decrements.

A computational model of language functions in flexible goal-directed behaviour

The function of language in high-order goal-directed human cognition is an important topic at the centre of current debates. Experimental evidence shows that inner speech, representing a self-directed form of language, empowers cognitive processes such as working memory, perception, categorization, and executive functions. Here we study the relations between inner speech and processes like feedback processing and cognitive flexibility. To this aim we propose a computational model that controls an artificial agent who uses inner speech to internally manipulate its representations. The agent is able to reproduce human behavioural data collected during the solution of the Wisconsin Card Sorting test, a neuropsychological test measuring cognitive flexibility, both in the basic condition and when a verbal shadowing protocol is used. The components of the model were systematically lesioned to clarify the specific impact of inner speech on the agent’s behaviour. The results indicate that inner speech improves the efficiency of internal representation manipulation. Specifically, it makes the representations linked to specific visual features more disentangled, thus improving the agent’s capacity to engage/disengage attention on stimulus features after positive/negative action outcomes. Overall, the model shows how inner speech could improve goal-directed internal manipulation of representations and enhance behavioural flexibility.

Predicting human perception of the urban environment in a spatiotemporal urban setting using locally acquired street view images and audio clips

This study investigates people's perception of visual and auditory landscapes in a mixed-use urban environment. A set of audio and visual data is collected at different intervals during the day in local streets with the help of an audio recorder and camera setup. The High and Low-level features from the collected audio and visual datasets are captured with the help of custom Deep Learning (DL) models and other standard algorithms. The collected data is used in the perception survey, which included human subjects (n = 73). The evaluation of the individual perception is done with the help of eight and six auditory and visual perceptual attributes, respectively. The results from the survey are then studied in relation to the features extracted from algorithms. Finally, a street of 10 km length is chosen within the study area where a spatiotemporal street-level visual and auditory data is collected. Statistical analysis and Machine Learning modeling are performed in the surveyed dataset to predict the human perception of audio and visual scenes in the chosen street. The results helped in understanding specific audio and visual features that are related to individual perceptions. Further, these relationships are utilized to create prediction models, which helped in creating spatiotemporal visual and auditory perception maps.

What you see is what you get: visual scanning failures of naturalistic social scenes in schizophrenia.

Impairments in social cognition contribute significantly to disability in schizophrenia patients (SzP). Perception of facial expressions is critical for social cognition. Intact perception requires an individual to visually scan a complex dynamic social scene for transiently moving facial expressions that may be relevant for understanding the scene. The relationship of visual scanning for these facial expressions and social cognition remains unknown. In 39 SzP and 27 healthy controls (HC), we used eye-tracking to examine the relationship between performance on The Awareness of Social Inference Test (TASIT), which tests social cognition using naturalistic video clips of social situations, and visual scanning, measuring each individual's relative to the mean of HC. We then examined the relationship of visual scanning to the specific visual features (motion, contrast, luminance, faces) within the video clips. TASIT performance was significantly impaired in SzP for trials involving sarcasm (p < 10-5). Visual scanning was significantly more variable in SzP than HC (p < 10-6), and predicted TASIT performance in HC (p = 0.02) but not SzP (p = 0.91), differing significantly between groups (p = 0.04). During the visual scanning, SzP were less likely to be viewing faces (p = 0.0001) and less likely to saccade to facial motion in peripheral vision (p = 0.008). SzP show highly significant deficits in the use of visual scanning of naturalistic social scenes to inform social cognition. Alterations in visual scanning patterns may originate from impaired processing of facial motion within peripheral vision. Overall, these results highlight the utility of naturalistic stimuli in the study of social cognition deficits in schizophrenia.

Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex.

Visual object identification requires both selectivity for specific visual features that are important to the object's identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognized as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we quantitatively describe the balance between selectivity and invariance in complex cells. Phase invariance is frequently partial, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.