Sight Research Articles

Clinical outcomes of a modified ophthalmic viscosurgical device-free implantable collamer lens implantation.

To investigate the efficacy and safety of a modified dual-incision ophthalmic viscosurgical device-free (OVD-free) method for implantable collamer lens (ICL) implantation in high myopic eyes. A total of 68 participants were enrolled in this prospective randomized clinical trial, including 33 in the OVD-free group and 35 in the standard group. Operation time and intraocular pressure (IOP) at 2 hours postoperatively were recorded. Visual acuity, refractive power, IOP, corneal endothelium parameters, and anterior segment parameters were assessed at 1 day, 1 week, 1 month, 3 months, and 6 months postoperatively. Postoperative subjective visual quality at 3 months was recorded through a Quality of Vision (QoV) questionnaire. No significant differences in visual acuity, refractive outcomes, and corneal endothelial parameters were found, while the operation time was significantly shorter in the OVD-free group. Both groups showed a significant increase in IOP at 2 hours after surgery, but the increase in the OVD-free group was significantly smaller than that in the standard group. In addition, the frequency of ring-shaped dysphotopsia in the OVD-free group (15.15%) was significantly lower than that in the standard group (40%), and the severity and annoyance of this symptom were also significantly lower in the OVD-free group. The modified OVD-free ICL implantation is a safe, effective, and predictable method for myopia correction, which could be a better choice for short surgery time, better subjective visual perception, and low occurrence of IOP elevation.

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.

Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) features, which can be used in any machine learning algorithm. Using electrocorticographic signals recorded during various movement and visual perception tasks, the sDM features were shown to improve the decoding accuracy and computational time compared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-γ power of the signals, but with higher trial-to-trial reproducibility. The proposed sDM features enable fast, accurate, and interpretable neural decoding.

An Exploration of China's Image Perception, Influencing Factors and Communication Strategies from a Latin American Perspective -An Empirical Study Based on the 2001-2023 Latin American Barometer -An Empirical Study Based on the 2001-2023 Latin American Barometer

An Exploration of China's Image Perception, Influencing Factors and Communication Strategies from a Latin American Perspective -An Empirical Study Based on the 2001-2023 Latin American Barometer -An Empirical Study Based on the 2001-2023 Latin American Barometer

Autonomous navigation and collision prediction of port channel based on computer vision and lidar.

This study aims to enhance the safety and efficiency of port navigation by reducing ship collision accidents, minimizing environmental risks, and optimizing waterways to increase port throughput. Initially, a three-dimensional map of the port's waterway, including data on water depth, rocks, and obstacles, is generated through laser radar scanning. Visual perception technology is adopted to process and identify the data for environmental awareness. Single Shot MultiBox Detector (SSD) is utilized to position ships and obstacles, while point cloud data create a comprehensive three-dimensional map. In order to improve the optimal navigation approach of the Rapidly-Exploring Random Tree (RRT), an artificial potential field method is employed. Additionally, the collision prediction model utilizes K-Means clustering to enhance the Faster R-CNN algorithm for predicting the paths of other ships and obstacles. The results indicate that the RRT enhanced by the artificial potential field method reduces the average path length (from 500 to 430m), average time consumption (from 30 to 22s), and maximum collision risk (from 15 to 8%). Moreover, the accuracy, recall rate, and F1 score of the K-Means + Faster R-CNN collision prediction model reach 92%, 88%, and 90%, respectively, outperforming other models. Overall, these findings underscore the substantial advantages of the proposed enhanced algorithm in autonomous navigation and collision prediction in port waterways.

Editorial: Perceptual organization in computer and biological vision

Editorial: Perceptual organization in computer and biological vision

Multi-Task Visual Perception for Object Detection and Semantic Segmentation in Intelligent Driving

Multi-Task Visual Perception for Object Detection and Semantic Segmentation in Intelligent Driving

Deep Learning Based Cystoscopy Image Enhancement.

Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex and diverse image degradation due to their underwater imaging characteristics. Among the various causes of image degradation, the blood haze resulting from bladder mucosal bleeding make the background blurry and unclear, severely affecting diagnostic efficiency, even leading to misjudgment. We propose a deep learning-based approach to mitigate the impact of blood haze on cystoscopy images. The approach consists of two parts: a blood haze removal network and a contrast enhancement algorithm. Firstly, we adopt Feature Fusion Attention Network (FFA-Net) and transfer learning in the field of deep learning to remove blood haze from cystoscopy images, and introduce perceptual loss to constrain the network for better visual results. Secondly, we enhance the image contrast by remapping the gray scale of the blood haze-free image and performing weighted fusion of the processed image and the original image. In the blood haze removal stage, the algorithm proposed in this paper achieves an average peak signal-to-noise ratio of 29.44 decibels, which is 15% higher than state-of-the-art traditional methods. The average structural similarity and perceptual image patch similarity reach 0.9269 and 0.1146, respectively, both superior to state-of-the-art traditional methods. Besides, our method is the best in keeping color balance after removing the blood haze. In the image enhancement stage, our algorithm enhances the contrast of vessels and tissues while preserving the original colors, expanding the dynamic range of the image. The deep learning-based cystoscopy images enhancement method is significantly better than other traditional methods in both qualitative and quantitative evaluation. The application of artificial intelligence will provide clearer, higher contrast cystoscopy images for medical diagnosis.

Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space.

Arthropods' eyes are effective biological vision systems for object tracking and wide field of view because of their structural uniqueness; however, unlike mammalian eyes, they can hardly acquire the depth information of a static object because of their monocular cues. Therefore, most arthropods rely on motion parallax to track the object in three-dimensional (3D) space. Uniquely, the praying mantis (Mantodea) uses both compound structured eyes and a form of stereopsis and is capable of achieving object recognition in 3D space. Here, by mimicking the vision system of the praying mantis using stereoscopically coupled artificial compound eyes, we demonstrated spatiotemporal object sensing and tracking in 3D space with a wide field of view. Furthermore, to achieve a fast response with minimal latency, data storage/transportation, and power consumption, we processed the visual information at the edge of the system using a synaptic device and a federated split learning algorithm. The designed and fabricated stereoscopic artificial compound eye provides energy-efficient and accurate spatiotemporal object sensing and optical flow tracking. It exhibits a root mean square error of 0.3 centimeter, consuming only approximately 4 millijoules for sensing and tracking. These results are more than 400 times lower than conventional complementary metal-oxide semiconductor-based imaging systems. Our biomimetic imager shows the potential of integrating nature's unique design using hardware and software codesigned technology toward capabilities of edge computing and sensing.

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

A Reconfigurable Bipolar Image Sensor for High-Efficiency Dynamic Vision Recognition.

Dynamic vision perception and processing (DVPP) is in high demand by booming edge artificial intelligence. However, existing imaging systems suffer from low efficiency or low compatibility with advanced machine vision techniques. Here, we propose a reconfigurable bipolar image sensor (RBIS) for in-sensor DVPP based on a two-dimensional WSe2/GeSe heterostructure device. Owing to the gate-tunable and reversible built-in electric field, its photoresponse shows bipolarity as being positive or negative. High-efficiency DVPP incorporating front-end RBIS and back-end CNN is then demonstrated. It shows a high recognition accuracy of over 94.9% on the derived DVS128 data set and requires much fewer neural network parameters than that without RBIS. Moreover, we demonstrate an optimized device with a vertically stacked structure and a stable nonvolatile bipolarity, which enables more efficient DVPP hardware. Our work demonstrates the potential of fabricating DVPP devices with a simple structure, high efficiency, and outputs compatible with advanced algorithms.

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease.

While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

Bionic Scotopic Adaptation Transistors for Nighttime Low Illumination Imaging.

Human vision excels in perceiving nighttime low illumination due to biological feedforward adaptation. Replicating this ability in biomimetic vision using solid-state devices has been highly sought after. However, emulating scotopic adaptation, entailing a confluence of efficient photoexcitation and dynamic carrier modulation, presents formidable challenges. Here, we demonstrate a low-power and bionic scotopic adaptation transistor by coupling a light-absorption layer and an electron-trapping layer at the bottom of the semiconducting channel, enabling simultaneous achievement of efficient generation of free photocarriers and adaptive carrier accumulation within a single device. This innovation empowers our transistor to exhibit sensitivity-potentiated characteristics after adaptation, detecting scotopic-level illumination (0.001 lx) with exceptional photosensitivity up to 103 at low voltages below 2 V. Moreover, we have successfully replicated diverse scotopic vision functions, encompassing time-dependent visual threshold enhancement, light intensity-dependent adaptation index, imaging contrast enhancement for nighttime low illumination imaging, opening an opportunity for artificial night vision.

Visual perception verbs in Old Anatolian Turkish

Abstract This study aims at describing the verbs of the visual sensory domain in Old Anatolian Turkish (oat), including basic and compound verb forms. We shall specifically focus on intra-field and trans-field meaning extensions of visual perception verbs such as baḳ-, naẓar et-, gör-, and görin-/gözük-. Our findings are based on a corpus consisting of 11 texts both in prose and poetry from the 13th to 15th centuries. There are mainly two types of verbs that conceptualise mental and emotive states through visual perception: one pertains to idiomatic expressions with göz ‘eye’, such as göz-(ün) aç- (eye-poss.acc open), göz-den düş- (eye-abl fall), and göz dut- (eye hold). The second form includes basic verbs including baḳ- (to look at), gör- (to see), naẓar et- (to look at) and gör-in-/göz-ük- (to appear). We shall show that visual perception constitutes a rich source for expressing emotive states, and the use of vision verbs for the expression of emotions is as productive as it is for mental states. Similar to Sweetser’s body is mind metaphor, we suggest that vision is emotion. The domain of visual perception in oat texts displays a strong connection to intellection; however, there is no evidence in our data indicating that the verbs gör- and baḳ- have meaning extension to ‘to know’. The phenomenon-based verbs görin- and gözük- have mainly two meaning extensions: one is related to physical existence, as in ‘occur’ and ‘appear’ in English. When these verbs co-occur with nouns and adjectives, they reflect the speakers’ judgements and beliefs by means of a metaphor.

Visual Perception of Regularity and the Composition Pattern Type of the Facade

The present study investigates the degree of visual regularity perceived by viewers in architectural compositions, specifically concerning the type of pattern used. The research is grounded in psychological and neuropsychological universal determinants of visual perception, particularly the perception of visual regularity. The study is based on an empirical survey that involved 48 participants who rated various compositions on a Likert scale. The stimuli presented consisted of a typology of compositional patterns of facades of Polish multifamily buildings developed by Malewczyk, Taraszkiewicz, and Czyż in 2022. The survey results were subjected to statistical analyses, which revealed a clear relationship between the type of composition and its perceived regularity. This implies that architects can predict the perceived regularity of a composition based on its type, which is crucial since visual regularity is closely linked to the sense of spatial order and aesthetic value. Both of these aspects are vital for perceiving architecture as a built environment. The study highlights the significance of visual perception in architectural design, particularly how the public perceives composition types.

Visual Perception and Multimodal Control: A Novel Approach to Designing an Intelligent Badminton Serving Device

Addressing the current issue of limited control methods for badminton serving devices, this paper proposes a vision-based multimodal control system and method for badminton serving. The system integrates computer vision recognition technology with traditional control methods for badminton serving devices. By installing vision capture devices on the serving device, the system identifies various human body postures. Based on the content of posture information, corresponding control signals are sent to adjust parameters such as launch angle and speed, enabling multiple modes of serving. Firstly, the hardware design for the badminton serving device is presented, including the design of the actuator module through 3D modeling. Simultaneously, an embedded development board circuit is designed to meet the requirements of multimodal control. Secondly, in the aspect of visual perception for human body recognition, an improved BlazePose candidate region posture recognition algorithm is proposed based on existing posture recognition algorithms. Furthermore, mappings between posture information and hand information are established to facilitate parameter conversion for the serving device under different postures. Finally, extensive experiments validate the feasibility and stability of the developed system and method.

PSYCHOLOGY OF DEVIANT FORMS OF ECONOMIC PERSONAL BEHAVIOR IN CYBERSPACE

The goal of the research achieved by the author is the analysis of psychological features of deviant forms of economic behavior of an individual in cyberspace. Methodology. To implement the problematic completeness of the research and obtain scientifically based and reliable results and general conclusions, a complex of theoretical methods was used: synthesis, logic and structure of the presentation, analysis and generalization, tools of scientific source studies. Linguistic methodology and the method of cognition were used to study the problematic issues of this work in the unity of their visual and factual perception, social content, legal and terminological form of representation. The systemic-structural approach made it possible to conceptually form, implement the theoretical foundations and model the complexity of the research discourse. Results. It is shown that human crime and delinquency are related to both external factors (interaction with other types of crime, age, gender, material values, influence of peers) and internal factors that mediate criminal tendencies. It was concluded that the specifics of deviant forms of individual economic behavior in cyberspace have not yet been sufficiently studied, and attention is focused on more common types of cyber deviance. Instead, the actual problems are the peculiarities of the formation of economic cyber deviance; the detection of a tendency to this type of behavior; planning correctional and educational psychological work with deviants and victims of economic cybercrimes. An important problem is the difficulty of conducting research taking into account the key feature of cyberspace - anonymity.

Hierarchical Constraints on the Distribution of Attention in Dynamic Displays

Human vision is remarkably good at recovering the latent hierarchical structure of dynamic scenes. Here, we explore how visual attention operates with this hierarchical motion representation. The way in which attention responds to surface physical features has been extensively explored. However, we know little about how the distribution of attention can be distorted by the latent hierarchical structure. To explore this topic, we conducted two experiments to investigate the relationship between minimal graph distance (MGD), one key factor in hierarchical representation, and attentional distribution. In Experiment 1, we constructed three hierarchical structures consisting of two moving objects with different MGDs. In Experiment 2, we generated three moving objects from one hierarchy to eliminate the influence of different structures. Attention was probed by the classic congruent–incongruent cueing paradigm. Our results show that the cueing effect is significantly smaller when the MGD between two objects is shorter, which suggests that attention is not evenly distributed across multiple moving objects but distorted by their latent hierarchical structure. As neither the latent structure nor the graph distance was part of the explicit task, our results also imply that both the construction of hierarchical representation and the attention to that representation are spontaneous and automatic.

Differences in the body image based on physical parameters among young women from the Czech Republic and Slovakia.

The increased prevalence of overweight and obesity in the Czech Republic and Slovakia has led to heightened emphasis on weight control, particularly among women. Our aim is to explore body image perceptions among women in both countries and compare their attitudes, focusing on the relationship between body image and body mass index (BMI), height, weight, body fat and the weight control. The cross-sectional study involved 358 female students from the University of Pardubice and Constantine the Philosopher University in Nitra, with equal representation from the Czech Republic and Slovakia. Body parameters were assessed using anthropometric methods and the InBody 230 diagnostic device, while participants' perceptions of their bodies were evaluated using the Body Shape Questionnaire. The results confirmed that BMI did not significantly influence self-perception among Slovak students, while Czech participants with increasing obesity tended to perceive themselves more negatively. Significantly higher median values for BMI (p = 0.0509), weight (p = 0.0507), height (p = 0.05) and body image (p = 0.002) were observed in the Czech Republic compared with Slovakia. No significant difference was found in weight control and fat between participants from both countries. Although obesity was demonstrated in both nations, body satisfaction was different for participants from the Czech Republic and Slovakia.

Engineering Graphene Phototransistors for High Dynamic Range Applications.

Phototransistors are light-sensitive devices featuring a high dynamic range, low-light detection, and mechanisms to adapt to different ambient light conditions. These features are of interest for bioinspired applications such as artificial and restored vision. In this work, we report on a graphene-based phototransistor exploiting the photogating effect that features picowatt- to microwatt-level photodetection, a dynamic range covering six orders of magnitude from 7 to 107 lux, and a responsivity of up to 4.7 × 103 A/W. The proposed device offers the highest dynamic range and lowest optical power detected compared to the state of the art in interfacial photogating and further operates air stably. These results have been achieved by a combination of multiple developments. For example, by optimizing the geometry of our devices with respect to the graphene channel aspect ratio and by introducing a semitransparent top-gate electrode, we report a factor 20-30 improvement in responsivity over unoptimized reference devices. Furthermore, we use a built-in dynamic range compression based on a partial logarithmic optical power dependence in combination with control of responsivity. These features enable adaptation to changing lighting conditions and support high dynamic range operation, similar to what is known in human visual perception. The enhanced performance of our devices therefore holds potential for bioinspired applications, such as retinal implants.

Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement

The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images.