Vision Perspective Research Articles

Comparative the effect of bisphenol A and bisphenol S on the development and spectral sensitivity of cone photoreceptors in zebrafish larvae (Danio rerio).

Color vision, which is mediated by cone photoreceptors in vertebrates, is essential for perceiving the external environment. Bisphenol A (BPA) and its substitute bisphenol S (BPS) have been widely used worldwide, while the evaluation of their safety, especially the newly discovered visual toxicity mechanism caused by them in recent years, has not been clearly explored. In the present study, we investigated the effects of BPA treatment (1, 10, and 100 μg/L) on cone cell development and function to evaluate visual toxicity. We also compared the mechanisms of color deficiency induced by BPA and BPS at the same concentrations. The results indicated that BPA (10 and 100 μg/L) caused the abnormal proliferation (increased number of cone cells), morphological abnormalities (increased height of cone cells), mosaic pattern disorder, and depressed expression of key genes related to the photo-transduction pathway, and impaired the light perception ability of both red and UV cones ultimately. Similar to the BPA exposure group, BPS (1, 10, and 100 μg/L) exposure resulted in structural damage and mosaic arrays disorder of red and UV cone photoreceptors. In contrast to BPA exposure, BPS exposure resulted in significant activation of key genes involved in the phototransduction pathway. Our data indicate that both BPS and BPA exposure can interfere with the development of cone cells, and two types of compounds disturb the transduction of photon signals within cone cells in different ways, which further impaired the retinal spectral sensitivity to the light signal. This study clarifies the root cause for color vision impairment induced by BPA from the perspective of cone-mediated color vision. It also clarified that the BPA and its substitute BPS may not be entirely safe at the single-cell level.

놀이중심 교육과정의 놀이기록에서 전문적 시각으로 살펴본 교사의 PCK

Objectives This study aims to explore the Pedagogical Content Knowledge (PCK) of teachers in the context of play-based early childhood education. The focus is on how teachers observe, document, and support play activities in educational settings that demand a high level of professional expertise. By analyzing teachers' play documentation from a professional vision perspective, the study seeks to identify what aspects teachers focus on during play situations and how their PCK is manifested. Methods This study analyzed 19 play documentation cases from the 2019 revised Nuri Curriculum to examine teachers' selective attention and knowledge-based reasoning. The analysis of selective attention focused on the specific elements of play that teachers observed, the aspects of children they concentrated on, the areas they emphasized to support and suggest during play, and the experiences and learnings they highlighted. For knowledge-based reasoning, the study assessed teachers' inferential actions based on observational evidence, their professional knowledge, and educational theories. Results First, the analysis revealed that teachers focused on the transformation of play through chance encounters and on all the relationships children form. Second, in the interplay of play and activities, the teachers' PCK was evident in their attuned listening to children's language and behavior while implementing their educational plans, as well as their pedagogical PCK in responding spontaneously to children's needs and supporting their learning. Conclusions The examination of teachers' PCK in play documentation indicated that it is an intertwining of educational knowledge, contextual (practical) knowledge, and knowledge about children. This results in the manifestation of PCK through planning, observation, support, and interpretation by teachers. The study highlighted the importance of teachers’ roles in implementing a play-based curriculum that aims to deepen, expand, and reorganize educational content and experiences to promote higher levels of learning and support.

Research on the health lighting scheme of university library reading room

The quality of the indoor lighting environment in the reading space of the university library is essential for students' visual perception, emotional evaluation, and cognitive efficiency. The design of the lighting environment considering the change of natural light is one of the important development directions to improve the light environment quality of the reading room and meet the health requirements of students. This paper from the perspective of reading vision, emotion, and cognitive probes into the natural light environment lighting design, builds a reading lighting environment laboratory, using subjective evaluation, task performance, and physiological indicators of the measurement method, first analyses the weight of different indicators under different time, establish lighting environment comprehensive evaluation index I and artificial illumination and desktop illumination prediction model. The experimental results show that experimental results show that visual discomfort and clarity are the key factors in the early and later stages. In the transition phase, emotional indicators become particularly important. The model of dynamic lighting design scheme during 17:30–20:30 is: y = 227753.1746–900120.63492x+ 1179428.57142x2-512000x3, R2 = 0.99702; the fitting formula of desktop illumination in 17:30–20:30 period is: y = 566.666667–197.08995x + 158.73016x2-37.03704x3, R2 = 0.95238. The results aim to provide a theoretical basis for the design and optimization of dynamic lighting under natural light in the library reading room.

Unsupervised Illumination Adaptation for Low-Light Vision.

Insufficient lighting poses challenges to both human and machine visual analytics. While existing low-light enhancement methods prioritize human visual perception, they often neglect machine vision and high-level semantics. In this paper, we make pioneering efforts to build an illumination enhancement model for high-level vision. Drawing inspiration from camera response functions, our model could enhance images from the machine vision perspective despite being lightweight in architecture and simple in formulation. We also introduce two approaches that leverage knowledge from base enhancement curves and self-supervised pretext tasks to train for different downstream normal-to-low-light adaptation scenarios. Our proposed framework overcomes the limitations of existing algorithms without requiring access to labeled data in low-light conditions. It facilitates more effective illumination restoration and feature alignment, significantly improving the performance of downstream tasks in a plug-and-play manner. This research advances the field of low-light machine analytics and broadly applies to various high-level vision tasks, including classification, face detection, optical flow estimation, and video action recognition.

Analysis of Research Trends in Mine Safety/Mining Safety (MS) from the Perspective of a Comparative Vision

Analysis of Research Trends in Mine Safety/Mining Safety (MS) from the Perspective of a Comparative Vision

Exploiting floral signals: Olfactory crypsis and visual attraction in crab spider predatory strategies

AbstractPredation exerts a profound influence on the evolution of camouflage and detection abilities in both predators and prey. For instance, flower‐visiting spiders need to ensure their concealment when ambushing on flowers, which compels pollinators to check any spider cues cautiously before landing. Although numerous studies have examined the visual camouflage of spiders deceiving bees, little is known regarding whether spiders also employ olfactory camouflage. Additionally, the detection of spiders by dipteran insects is often overlooked, despite them being major non‐bee pollinators. Here, we explored the detection ability of dipteran pollinators and the crypsis skill of spiders from both olfactory and vision perspectives using the housefly Musca domestica L. (Diptera: Muscidae) as prey, the crab spider Ebrechtella tricuspidata (Fabricius) (Araneae: Thomisidae) as predators and chamomile Matricaria recutita L. (Asteraceae) as substrates. Our olfactory experiments revealed that experienced houseflies could respond to and avoid spider odour. However, this response did not appear to be innate, as naive individuals showed no aversion. Moreover, experienced houseflies did not have any avoidance behaviours to the mixed odours of spider and flower, indicating that spiders achieved olfactory crypsis utilizing floral scent. Our vision experiments and visual modelling demonstrated that houseflies could detect spiders positioned on flower. Surprisingly, instead of avoiding them, houseflies exhibited a preference for flowers occupied by female spiders, suggesting the attractive nature of female spider colouration. This paper provides evidence for the first time that crab spiders use floral scent to achieve olfactory crypsis and proposes a potential yellow‐signalling mechanism for crab spiders to attract insects.

Compressive strength detection of tunnel lining using hyperspectral images and machine learning

Traditional tunnel lining strength detection techniques are mostly contact-based, with relatively low detection efficiency. This study innovatively proposes hyperspectral imaging method to rapidly detect tunnel concrete lining strength from a machine vision perspective. Hyperspectral cameras were employed in indoor experiments to capture hyperspectral images of concrete specimens with different compressive strength levels. The differences of concrete strength based on hyperspectral reflectance characteristics were analysed using hyperspectral images and machine learning algorithms. Firstly, the K-Nearest Neighbors (KNN) classification algorithm was used to predict the classification of the concrete hyperspectral dataset with accuracy mostly exceeding 90 %. The results indicate distinctive differences in hyperspectral reflectance characteristics among concrete specimens. Furthermore, compressive strength prediction of different concrete specimens was carried out using Principal component regression (PCR), Partial least squares regression (PLSR), and Least Squares Support Vector Machine (LSSVM) machine learning models on both original and Savitzky-Golay(S-G) processed spectral data. LSSVM and PLSR models performed excellently in the visible light spectrums(400–1000 nm), with LSSVM excelling in the near-infrared spectrums(900–1700 nm). Finally, the feasibility of using Hyperspectral imaging(HSI) technology to detect tunnel lining strength was demonstrated at the shield tunnel model site. The predicted results were validated by combining with strength values measured by the rebound hammer, and presented promoising research path for automated non-destructive detection of concrete tunnel lining strength.

The Effectiveness of Future Perspective Education on Academic Hope and Goal Orientation in 11th Grade Students

Aim: The present study was conducted with the aim of determining the effectiveness of future perspective education on goal orientation and academic hope in 11th grade male students of Baghmalek city (Iran). Methods: The current experimental research design included a pre-test and post-test experiment with a control group. The population of this research included all 11th grade male students in Baghmalek city in the academic year of 1400-1401 (persian calendar). Among these individuals, 48 ​​persons who met the criteria for entering the research were selected through multi-stage random sampling method and were placed in two experimental (n=24) and control (n=24) groups. In this research, Goal Orientation Questionnaire and Education Hope Questionnaire were used. After the pre-test, the experimental group received the educational perspective of the future vision during nine 60 to 90 minutes sessions. Then a post-test was taken from both groups. Findings: The results of multivariate and univariate covariance analysis showed that future perspective education had a significant effect on the dimensions of goal orientation (dominant, dominant-avoidant, performance-oriented and performance-avoidant) and academic hope in 11th grade male students (p > 0.005). Conclusion: In general, the results of this research showed that teaching the future perspective is effective on goal orientation and educational hope and can be used to improve the academic hope and goal orientation of 11th grade male students.

Video Coding for Machines: Compact Visual Representation Compression for Intelligent Collaborative Analytics.

As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

A multi-featured expression recognition model incorporating attention mechanism and object detection structure for psychological problem diagnosis

Expression is the main method for judging the emotional state and psychological condition of the human body, and the prediction of changes in facial expressions can effectively determine the mental health of a person, thus avoiding serious psychological or psychiatric disorders due to early negligence. From a computer vision perspective, most researchers have focused on studying facial expression analysis, and in some cases, body posture is also considered. However their performance is more limited under unconstrained natural conditions, which requires more information to be used in human emotion analysis. In this paper, we design an Adaptive Multi-End Fusion Attention Mechanism suitable for extracting human body information based on the deep learning framework, depending on human expressions, postures and the environment they are in and add it to an object detection model to obtain the information we need from different regions of the human body and face and features of different sizes and use fusion networks for feature fusion and classification, and from different test methods to confirm that this fusion approach for expression recognition and prediction is feasible. This model achieves an average accuracy of 34.51 % in the Emotic contextual expression recognition dataset.

Research on Spatial Design and Reconstruction Strategy of Aging Based on Visual Features

At present, our country is in the rapid development stage of aging, the pension problem has always been a problem that the whole society pays high attention to, how to create a high quality living environment for the old people becomes a new research topic. From the perspective of vision, combined with architectural design theory, geriatric psychology, color psychology and other theories, this paper explores the interior public space environment design of elderly buildings from the aspects of light, color, quality, humanity and other influencing factors. At the same time, it analyzes the living situation of the old apartment and the characteristics of the old people in our country. From the aspects of space environment, function layout and so on, this paper summarizes the renovation strategy of interior space of elderly apartment. It provides a new reference for the design of elderly apartments in China in the future, and combines the design of elderly service centers to enrich the design methods of indoor public space environment of elderly care buildings in China in the future, so as to improve the quality of life of the elderly.

Super-resolution reconstruction of 3D digital rocks by deep neural networks

Digital rock technology provides valuable insights into the pore structure and fluid flow properties of geoenergy resources. Artificial intelligence technology in vision and image processing, especially the image super-resolution, has great potential for digital rock reconstruction and resolution enhancement. However, the analyzed core samples are typically sandstones/carbonates in micro-scale resolutions and in two-dimensional (2D) space, whereas the shale rocks in nano-scale resolutions for unconventional resources or three-dimensional (3D) digital cores are rarely investigated. Additionally, previous studies primarily emphasized image quality from a computer vision perspective, with little consideration given to estimating physical properties of digital rocks using super-resolution techniques.This study presents a very deep super-resolution (VDSR) algorithm, specifically designed to generate high-resolution 3D digital rock images, for nano-scale shale matrix and micro-scale hydraulic fractures. We compare both image quality and permeability accuracy between the original high-resolution images and the super-resolution images reconstructed by the proposed method. The results reveal that the reconstructed images using the proposed method closely resemble the actual images, and effectively reduce errors in permeability computations. This study highlights the applicability of the proposed VDSR algorithm in establishing the detailed structures of 3D nano-scale shale matrix and hydraulic fractured rocks, thus advancing super-resolution techniques in digital core analysis for geoenergy resources development.

Delving into Multimodal Prompting for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and large intra-class variations. However, prevailing approaches primarily focus on uni-modal visual concepts. Recent advancements in pre-trained vision-language models have demonstrated remarkable performance in various high-level vision tasks, yet the applicability of such models to FGVC tasks remains uncertain. In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a multimodal prompts scheme and a multimodal adaptation scheme. The former includes Subcategory-specific Vision Prompt (SsVP) and Discrepancy-aware Text Prompt (DaTP), which explicitly highlights the subcategory-specific discrepancies from the perspectives of both vision and language. The latter aligns the vision and text prompting elements in a common semantic space, facilitating cross-modal collaborative reasoning through a Vision-Language Fusion Module (VLFM) for further improvement on FGVC. Moreover, we tailor a two-stage optimization strategy for MP-FGVC to fully leverage the pre-trained CLIP model and expedite efficient adaptation for FGVC. Extensive experiments conducted on four FGVC datasets demonstrate the effectiveness of our MP-FGVC.

COVID-19 mortality paradox (United States vs Africa): Mass vaccination vs early treatment.

The coronavirus disease 2019 (COVID-19) mortality rate in 55 African countries is almost 4.5 times lower than in the coronavirus disease 2019 (COVID-19) despite Africa having over 4.2 times more people. This mortality paradox is also evident when comparing Nigeria, a heavily populated, poorly vaccinated and weakly mandated country to Israel, a small, highly vaccinated and strictly mandated country. Nigeria has almost 4 times lower COVID mortality than Israel. In this Field of Vision perspective, I explain how this paradox has evolved drawing upon my academic, clinical and social experience. Since April 2020, I've developed and been using the Egyptian immune-modulatory Kelleni's protocol to manage COVID-19 patients including pediatric, geriatric, pregnant, immune-compromised and other individuals suffering from multiple comorbidities. It's unfortunate that severe acute respiratory syndrome coronavirus 2 is still evolving accompanied by more deaths. However in Africa, we've been able to live without anxiety or mandates throughout the pandemic because we trust science and adopted early treatment using safe, and effective repurposed drugs that have saved the majority of COVID-19 patients. This article represents an African and Egyptian tale of honor.

Learning Structured Relation Embeddings for Fine-Grained Fashion Attribute Recognition

Fashion attribute recognition is a not-new topic, but rather a core task in understanding fashion from the perspective of computer vision. This paper proposes a structured relation-aware network (sRA-Net), which exploits multiple hidden relations in fashion images to enrich and achieve accurate attribute representations to boost the performance of fashion attribute recognition. Specifically, it deconstructs the features of a clothing fashion item into three levels, including low-level attribute-related image region information, mid-level attribute dependency information, and high-level clothing look information. To learn these multi-relational embeddings, we present three relation-aware attention mechanisms. The attribute attention mechanism describes the relationship among different attribute vectors through self-attention and uses the attention map to update the attribute embedding. Then, the spatial attention mechanism associates the attribute with the image features and enhances the attribute embedding by leveraging the attribute-related image region. Finally, the channel attention mechanism selects attribute-related image feature channels to obtain a more fine-grained attribute embedding. Furthermore, we introduce structure-aware embedding to constrain attribute recognition in images from a global perspective by identifying the inner structure of the clothing. Without bells and whistles, sRA-Net outperforms all state-of-the-art attribute recognition methods on two mainstream fashion attribute datasets, namely the DeepFashion-C dataset and iFashion-Attribute dataset, with over 1%-3% improvement.

Evaluation of Students’ Learning Engagement in Online Classes Based on Multimodal Vision Perspective

The method of evaluating student engagement in online classrooms can provide a timely alert to learners who are distracted, effectively improving classroom learning efficiency. Based on data from online classroom scenarios, a cascaded analysis network model integrating gaze estimation, facial expression recognition, and action recognition is constructed to recognize student attention and grade engagement levels, thereby assessing the level of student engagement in online classrooms. Comparative experiments with the LRCN model, C3D network model, etc., demonstrate the effectiveness of the cascaded analysis network model in evaluating engagement, with evaluations being more accurate than other models. The method of evaluating student engagement in online classrooms compensates for the shortcomings of single-method evaluation models in detecting student engagement in classrooms.

Exploring Potential Directions: The Transformative Impact of Integrating Psychology into Design for Well-Being

Interdisciplinary design research is recognised as a transformative approach to design that enhances the quality of work, improves products and services, enhances the user experience and promotes social inclusion. Integrating psychology into design for well-being involves leveraging insights from psychological research to inform and enhance the design process, ultimately creating products, services, and environments that promote mental and emotional health. This paper systematically explores the impact of the three core themes of vision and perception, mind flow and creativity, and art therapy on individuals and society. Firstly, the fundamental role of visual arts in psychotherapy is elucidated from the perspective of vision and perception. In addition, the article delves into the intricate relationship between mind flow states and creativity, emphasising their importance in problem solving and generating ideas. The article further introduces the basic concepts of art therapy, emphasising its positive effects on mental health, stress reduction, trauma recovery and cognitive functioning. In summary, this article summarises and reviews the literature on integrative applications that fit these themes, demonstrating the methods and possibilities of connecting current research themes that differ at the design level. In conclusion, the synergistic integration of vision, mindfulness flow, and art therapy contributes to a more refined, humane, and impactful design paradigm.

Study on InSAR image fusion for improved visualization of active landslides in alpine valley areas: A case in the Batang Region, China

InSAR observation with a wide range and high sensitivity is one of the main techniques for active landslide identification, but due to the influence of the SAR satellite side-view imaging method and observation angle, single-track images in alpine valley areas facing geometric distortion, such as shadowing, layover, and foreshortening, which leads to ineffective observation in some areas. Combining the ascending and descending observation results is expected to compensate for this deficiency to a large extent. This work constructs a pixel-level image fusion method to achieve the fusion of InSAR ascending and descending orbit deformation results, consisting of three algorithms: the DMPI (decision matrix-guided pixel-level InSAR image fusion algorithm), the DMFS (deformable median filter based on decision feedback), and the QSDS (superpixel-based dual-space InSAR image fusion quality evaluation algorithm). To verify the effect, the Batang section of the Jinsha River with high mountain canyon terrain and frequent active landslides is selected. The results show that using the DMPI fused InSAR ascending and descending orbit images can improve spatial perception by enhancing the texture gradient value of remote sensing images by a maximum of two orders of magnitude, compared with the single-track InSAR observation images. Meanwhile, the number of landslides identified increased by 31.5%, and the total area increased by 50.9% on average, improving the accuracy of target detection and identification. In noise reduction, DMFS improves fusion image readability and feature detail by removing SPN (salt and pepper noise) while preserving target edge detail. Furthermore, the QSDS which can evaluate the quality of InSAR fusion images from the perspective of human eye vision, provides an adequate basis for determining the interpretability of InSAR images. This fusion method can partially improve the question of incomplete ground observation due to radar line of sight occlusion in the high mountain canyon area, thus better presenting the InSAR observation results and helping to comprehensively and conveniently interpret the InSAR deformers.

A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.

Spatial sight analysis of Hangzhou Xiaoyingzhou based on tourists' landscape preference

Tourist preferences are important for the high-quality planning and design of recreation spaces. The famous scenic locale of West Lake in Hangzhou, China, is used as an example in this study. Based on multi-source data (e.g., online comments, and tourist photographs), we used content analysis, kernel density estimation, and image semantic segmentation technology to determine the spatial distribution of tourists' landscape preferences. We analyzed these spatial sight characteristics from the viewpoint, sight distance, and perspectives. The results show that tourists' landscape preferences are mainly concentrated on landscape architecture for recreation. The viewpoints of these preferences are concentrated in the north-south embankment—far beyond that of the east-west embankment. The preferences also show a spatial sequence in terms of sight distance, and the best visual effect is the open platform at the north and south islands of Xiaoyingzhou. From the perspective of vision, the degree of spatial openness in the tourists' landscape preferences is proportional to the distance of vision; the two factors have a convergent relationship. The discussion of the characteristics of tourists’ landscape preference and space sight creation in Xiaoyingzhou provides a reference for the quality improvement of island recreation space.