Sight Research Articles

Effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with strabismus.

To observe the effects of visual neuroplasticity training on visual perception, visual quality, and macular blood flow in patients with concomitant strabismus postoperatively. In total, 108 patients underwent binocular strabismus correction operation, and some patients underwent neuroplasticity training. All patients underwent clinical ophthalmic examination, including measurement of best-corrected visual acuity, spherical equivalent, axis length, optical coherence tomography angiography, optical quality analysis system, and visual perception examinations. A total of 78 patients received neuroplasticity training for 1 month postoperatively, and 30 patients did not receive training. All patients underwent a visual perception examination preoperatively and at 1 day, 1 week, and 1 month postoperatively. Macular blood flow and visual quality were examined preoperatively and at 1 month postoperatively. Postoperative visual perception was better than preoperative visual perception (P < 0.05). After neuroplasticity training, the visual perception of the trained subjects was better than that of the untrained subjects (P < 0.05), and the blood flow in the macular area of the trained patients was lower than that of the untrained subjects (P < 0.05). The visual quality of the untrained subjects was lower than that of the trained patients (P < 0.05). Visual inspection system could accurately evaluate binocular visual perception in patients with concomitant strabismus. After surgical alignment of the strabismus patient, training can stimulate and integrate the formation of stereovision in a short period of time, maintain the visual quality of patients after surgery, and provide conditions for the formation of binocular visual signals and binocular stereovision, but in the short term, it will lead to the decrease of macular blood vessel density and perfusion density. However, the long-term effects of training have not been proven.

Structurally-constrained encoding framework using a multi-voxel reduced-rank latent model for human natural vision.

Voxel-wise visual encoding models based on convolutional neural networks (CNNs) have emerged as one of the prominent predictive tools of human brain activity via functional magnetic resonance imaging (fMRI) signals. While CNN-based models imitate the hierarchical structure of the human visual cortex to generate explainable features in response to natural visual stimuli, there is still a need for a brain-inspired model to predict brain responses accurately based on biomedical data. To bridge this gap, we propose a response prediction module called the Structurally Constrained Multi-Output (SCMO) module to include homologous correlations that arise between a group of voxels in a cortical region and predict more accurate responses. This module employs all the responses across a visual area to predict individual voxel-wise BOLD responses and therefore accounts for the population activity and collective behavior of voxels. Such a module can determine the relationships within each visual region by creating a structure matrix that represents the underlying voxel-to-voxel interactions. Moreover, since each response module in visual encoding tasks relies on the image features, we conducted experiments using two different feature extraction modules to assess the predictive performance of our proposed module. Specifically, we employed a recurrent CNN that integrates both feedforward and recurrent interactions, as well as the popular AlexNet model that utilizes feedforward connections. In the end, we demonstrate that the proposed framework provides a reliable predictive ability to generate brain responses across multiple areas, outperforming benchmark models in terms of stability and coherency of features.

Challenges of parenting children born before 24 weeks of gestation.

To assess experience of care, well-being of parents and children's development in a cohort of extremely premature infants born <24 weeks of gestation in Sweden from 2007 to 2018. A survey based on multiple questionnaires answered by 124/349 (35.5%) parents. The median age of parents and children was 43 and 9 years, respectively; 74.2% were mothers. Parents expressed high healthcare satisfaction. Following discharge from neonatal care, the satisfaction with the infant's treatment, support from personnel and being respected as a parent significantly declined but remained high. The criteria for suspected developmental deviation according to the screening test early symptomatic syndromes eliciting neurodevelopmental clinical examinations-questionnaire was fulfilled by 84.3%, 55.6% had suspected avoidant restrictive food intake disorder and 47.9% had visual perception problems. Parents experienced severe fatigue (48.6%) despite strong social support and family self-efficacy. Economic support was provided to 30.6%, and 37.9% of children were enrolled in habilitation services. This study highlighted the substantial challenges faced by parents of infants born before 24 weeks of gestation, including decreased satisfaction post-discharge, fatigue and concerns about children's well-being. The findings underscore the need for comprehensive family-centred support and long-term multi-professional follow-up centres.

Neural activity shaping in visual prostheses with deep learning.

&#xD;The visual perception provided by retinal prostheses is limited by the overlapping current spread of adjacent electrodes. This reduces the spatial resolution attainable with unipolar stimulation. Conversely, simultaneous multipolar stimulation guided by the measured neural responses - Neural Activity Shaping (NAS) - can attenuate excessive spread of excitation allowing for more precise control over the pattern of neural activation. However, predicting the results of a multipolar stimulus pattern is a challenging task. Previous attempts focused on analytical solutions based on an assumed linear nonlinear model of retinal response; an Analytical Model Inversion (AMI) approach. Here, we propose a model-free solution for NAS, using Artificial Neural Networks (ANNs) that could be trained with data acquired from the implant. Our method consists of two ANNs trained sequentially. The Measurement Predictor Network (MPN) is trained on data from the implant and is used to predict how the retina responds to multipolar stimulation. The Stimulus Generator Network (STG) is trained on a large dataset of natural images and uses the trained MPN to determine efficient multipolar stimulus patterns by learning its inverse model. We validate our method in silico using a realistic model of retinal response to multipolar stimulation.&#xD;Main Results:&#xD;We show that our ANN-based NAS approach produces sharper retinal activations than the conventional unipolar stimulation strategy. As a theoretical bench-mark of optimal NAS results, we implemented AMI stimulation by inverting the model used to simulate the retina. Our ANN strategy produced equivalent results to AMI, while not being restricted to any specific type of retina model and being three orders of magnitude more computationally efficient. Our novel protocol provides a method for efficient and personalized retinal stimulation, which may improve the visual experience and quality of life of retinal prosthesis users.&#xD.

Reading Stanislavsky from a Stage Lighting Designer’s Point of View: Stanislavsky’s Notes on the Dramaturgy of Light

ABSTRACT Stage lighting design is, mostly, referenced and researched in correlation to the scenographic history of theatre, often leaving in shadow the organic impact it has on the human beings involved in the theatre process, both in front and behind the footlights. Light, though, is not just a visual scenographic element; it is living, an orchestrator of the human vision; we still say, “I‘m going to see a play” and not hear a text spoken on stage. The beginning of the twentieth century was a turning point for theatre making, with Stanislavsky being one reference point for future developments. He made valuable remarks about lighting, proving that concentrating attention is an active process that does not happen by accident and that there are tools, like lighting, to facilitate that process. Key to my discussion here will be Stanislavsky’s notion of a light-initiated compartmentalisation and manipulation of stage space, i.e. the identification of specific sections on the stage the limits of which are set by light: circles of attention. Stanislavsky’s circles of attention support the statement that lighting serves as a “magnifying lens” that draws the viewer’s focus to particular areas or actions on stage. This article will elaborate on the potential of Stanislavsky’s circles of attention to the dramaturgy of light, shedding light on various extracts of his writings where the dynamic functions of stage lighting are observed and described in an experiential way.

Association between caregiver and infant visual neurocognition

Previous work has shown that caregiver attention shapes visual cognition in infants through dyadic interactions. Is this association measurable when visual cognition is objectively measured in caregivers and infants using comparable experimental paradigms? In the current study, we presented infants (N = 86) and caregivers (N = 78) with age-specific variants of the same preferential looking visual cognition task to investigate whether caregiver visual cognition was associated with their infants’ visual cognition. In each trial of the task, two side-by-side flashing displays of coloured shapes were presented. On the ‘unchanging’ side, the colours of the shapes remained the same. On the ‘changing’ side, the colour of one shape changed after each flash. Load was varied by changing the number of shapes across trials (low, medium, and high loads). We extracted looking dynamics using video recordings and brain function using functional near-infrared spectroscopy as both infants and caregivers engaged with the task. Change preference (CP) score, which represented the amount of time spent looking at the changing side divided by the total looking duration, showed a load-dependent modulation for both infants and caregivers. Both groups showed the highest CP scores at the low load. Further, higher caregiver CP scores was associated with higher infant CP scores at the low load. Both infants and caregivers engaged canonical regions of the fronto-parietal network involved in visual cognition. Critically, higher caregiver CP scores were associated with greater activation in the left superior parietal lobule in younger infants, a region involved in allocating visuo-spatial attention and working memory maintenance. Further, there was spatial overlap between performance-dependent regions in the right parietal cortex in caregivers and younger infants. Our findings provide first evidence of a heritability-related visual neurocognitive association between caregivers and their children in the first year of life.

Quality assessment for multi-exposure fusion light field images with dynamic region segmentation

The luminance dynamic range of real scenes can reach up to nine orders, whereas the existing light field camera captures light field images (LFIs) with a limited luminance dynamic range of about two orders. Although LFIs can be enhanced using multi-exposure fusion (MEF) technologies, this process introduces various distortions that affect human visual perception. Therefore, it is crucial to develop an effective quality assessment tool for multi-exposure fusion light field images (MEF-LFIs). Starting from the complex distortion characteristics generated by MEF with dynamic scenes, this paper proposes an MEF-LFI quality metric with dynamic region segmentation. The proposed metric consists of three main modules. The first module focuses on spatial feature extraction and incorporates a dynamic region segmentation scheme to address artifacts in MEF-LFIs. The second module utilizes Shearlet transform for spectrum feature extraction to characterize detail loss. Additionally, the third module addresses distortion in the angular domain of MEF-LFIs through angular feature extraction using epipolar plane images and tensor decomposition. Finally, the quality of the MEF-LFIs is evaluated by integrating spatial features, spectrum features and angular features into feature vectors which are then inputted into a regression model. The experimental results show that the proposed metric is superior to the representative quality metrics and has better consistency with the human visual perception.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

The role of motor effort on the sensorimotor number system.

The integration of numerical information with motor processes has emerged as a fascinating area of investigation in both animal and human cognition. The interest in a sensorimotor number system has recently generated neurophysiological and psychophysical evidence which combine to highlight the importance of motor functions in the encoding of numerical information. Nevertheless, several key questions remain, such as the influence of non-numerical motor parameters over numerical perception. Here we tested the role of physical effort, a parameter positively correlated with the number of actions, in modulating the link between hand-actions and visual numerosity perception. Effort was manipulated during sensorimotor adaptation as well as during a new actions-estimation paradigm. The results of Experiment 1 shows that physical effort in the absence of actions (passive effort) is not sufficient to activate the sensorimotor number system, indicating that self-produced actions are instead necessary. Further experiments demonstrated that effort is marginally integrated during motor adaptation (Experiment 2) but discarded when estimating the number of self-produced hand actions (Experiment 3). Overall, the results indicate that the sensorimotor number system is largely fed by the number of discrete actions rather than the amount of effort but also indicates that effort (under specific circumstances) might be integrated. These findings provide novel insights into the sensorimotor numerical integration, paving the way for future investigations, such as on its functional role.

DeCoGAN: MVCT image denoising via coupled generative adversarial network

Objective. In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method. Approach. We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising. Main Results. Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance. Significance. The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy.

A Historical Approach to Understanding Differentiation of RYB vs RGB

This article defines the roles of colors and presents their historical development as a fundamental means of expression within artwork. It also introduces color theorists and practices, including the mixing of pigments, a necessary technique for artists-designers in creating tangible results. Modern artists-designers have used these theorists to express themselves in producing an aesthetic experience in the viewer through their artwork, starting with Sir Isaac Newton’s light-spectrum experiments using a glass prism and a beam of white light, and continuing through Johannes Itten’s color-wheel and creative color-matching exercises. This article objective defines the significance of color, its historical development on the basis of earlier perceptual theorists’ findings that color acquires dimension and meaning in response to cultural-historical context or psychological realities. As a result, this paper also explores how color affects our emotions, our psychic and spiritual manifestations. This psychic-spiritual manifestation is revealed through a process of visual perception and experimenting with color and its symbolic meaning. Artists such as Seurat, Munch, Kandinsky, Paul Klee, Josef Albers, Picasso, Joan Miro, or Mark Rothko demonstrated the expressive power of color-pigment. The authors hope that this paper will bring significant discussion, awareness, will assist general readers and academic researchers and truly advance knowledge.

Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction

Wheeled lifting robots have found widespread applications in various industrial and logistical environments. However, traditional robots are far from adequate in terms of visual perception capabilities. Additionally, their remote control methods suffer from inefficiencies, which tend to bring safety concerns. To address these issues, this work proposes an autonomous multi-sensor-enabled wheeled lifting robot system, i.e., AMSeWL-R, to facilitate remote autonomous operations. Specifically, AMSeWL-R integrates real-time simultaneous localization and mapping with object detection on a wheeled lifting robot. Additionally, a novel mobile-ROS interaction method is proposed to achieve real-time communication and control between a mobile device and a ROS host. Furthermore, a lightweight object detection algorithm based on YOLOv8, i.e., YOLOv8-R, is proposed to achieve faster detection. Experimental results validate the effectiveness of the AMSeWL-R system for accurately detecting objects and mapping its surroundings. Furthermore, TensorRT acceleration is employed during practical testing on a Jetson Nano to achieve real-time detection using the proposed YOLOv8-R, demonstrating its efficacy in real-world scenarios.

Network mechanisms of ongoing brain activity’s influence on conscious visual perception

Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity’s influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.

Questionnaire to Survey Cosmetic Outcomes in Laparoscopic Surgery for Colorectal Cancer

Background and objectives: There has been a steady increase in the use of minimally invasive surgery, including conventional multiport laparoscopic surgery (MLS) and single-site laparoscopic surgery (SLS) for colorectal cancer. We aimed to evaluate how important the cosmetic outcome, one of the advantages of SLS, is to patients and whether SLS reflects social needs. Methods: We used a web-based questionnaire to survey nonmedical and medical workers for what factors were considered on the assumption that respondents undergo colorectal cancer surgery and that the most important person for them undergoes. Five items (curability, safety, pain, length of hospital stay, and cosmetic outcomes) were compared. After paired photographs before and after SLS and MLS were shown, perceptions of body image and cosmesis were assessed using a visual analog scale. Results: This study included a total of 1352 respondents (990 nonmedical and 362 medical). Curability had the highest score (49.9–53.7 points), followed by safety (23.8–24.7 points). The scores for cosmetic outcomes (6.2–7.1 points) were almost equal to those of the length of hospital stay (6.2–7.1 points), which was associated with medical costs and pain (10.0–11.1 points), one of the main reasons for fear of surgery. Participants who were female, younger, and in the nonmedical group placed great importance on cosmetic outcomes. For all questions regarding body image and cosmesis, SLS had superior scores compared with MLS. Conclusions: Understandably, curability, and safety were most important in colorectal cancer surgery. However, medical workers should consider cosmetic outcomes, even in malignant cases.

Real-time representation and rendering of high-resolution 3D light field based on texture-enhanced optical flow prediction

Three-dimensional (3D) light field displays can provide an immersive visual perception and have attracted widespread attention, especially in 3D light field communications, where 3D light field displays can provide face-to-face communication experiences. However, due to limitations in 3D reconstruction and dense views rendering efficiency, generating high-quality 3D light field content in real-time remains a challenge. Traditional 3D light field capturing and reconstruction methods suffer from high reconstruction complexity and low rendering efficiency. Here, a Real-time optical flow representation for the high-resolution light field is proposed. Based on the principle of 3D light field display, we use optical flow to ray trace and multiplex sparse view pixels. We simultaneously synthesize 3D light field images during the real-time interpolation process of views. In addition, we built a complete capturing-display system to verify the effectiveness of our method. The experiments' results show that the proposed method can synthesize 8 K 3D light field videos containing 100 views in real-time. The PSNR of the virtual views is around 32 dB and SSIM is over 0.99, and the rendered frame rate is 32 fps. Qualitative experimental results show that this method can be used for high-resolution 3D light field communication.

Merleau-Ponty's Cézanne as Misfit Artist

This paper explores Cézanne’s art through the lens of disability gain. Disability gain defies the ability-disability binary, which defines disability as a lack of ability, by emphasizing what is gained through different disabilities. Central to my discussion are (1) Tobin Siebers’s description of modern art as vitally and thematically disabled and (2) Rosemarie Garland-Thomson’s concept of misfitting, which allows for a phenomenological account of disability that emphasizes the depth of awareness and creative world-making possibilities that are gained through disability. By bringing Siebers’s approach to disability aesthetics and Garland-Thomson’s concept of misfitting into conversation with Merleau-Ponty’s “Cézanne’s Doubt” and “Eye and Mind,” I will explain how Cézanne’s unique way of perceiving the world and capturing it in paint helps Merleau-Ponty to dismantle abstract, disembodied concepts of visual perception and trace the nuanced contours of lived perspective. In other words, what is gained through Cézanne’s disabilities is a revolutionary optics that overcomes the limitations of Cartesian optics.

Assessment of Body Image Perception and Quality of Life among Oncology Patients

Assessment of Body Image Perception and Quality of Life among Oncology Patients

Image stitching and target perception for Autonomous Underwater Vehicle-collected side-scan sonar images

Image stitching and target perception for Autonomous Underwater Vehicle-collected side-scan sonar images

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.

The modelling of flower colour: spectral purity or colour contrast as biologically relevant descriptors of flower colour signals for bees depending upon the perceptual task.

Flower colour is an important mediator of plant-pollinator interactions. While the reflectance of light from the flower surface and background are governed by physical properties, the perceptual interpretation of such information is generated by complex multilayered visual processing. Should quantitative modelling of flower signals strive for repeatable consistency enabled by parameter simplification, or should modelling reflect the dynamic way in which bees are known to process signals? We discuss why colour is an interpretation of spectral information by the brain of an animal. Different species, or individuals within a species, may respond differently to colour signals depending on sensory apparatus and/or individual experience. Humans and bees have different spectral ranges, but colour theory is strongly rooted in human colour perception and many principles of colour vision appear to be common. We discuss bee colour perception based on physiological, neuroanatomical and behavioural evidence to provide a pathway for modelling flower colours. We examine whether flower petals and floral guides as viewed against spectrally different backgrounds should be considered as a simple colour contrast problem or require a more dynamic consideration of how bees make perceptual decisions. We discuss that plants such as deceptive orchids may present signals to exploit bee perception, whilst many plants do provide honest signalling where perceived saturation indicates the probability of collecting nutritional rewards towards the centre of a flower that then facilitates effective pollination.