Visual Mechanisms Research Articles

Bidirectional Feature Aggregation Network for Stereo Image Quality Assessment Considering Parallax Attention-Based Binocular Fusion

Inspired by the two-path visual information processing mechanism (i.e., a bottom-up path and a top-down path), we propose a bidirectional binocular feature aggregation based stereo image quality assessment (SIQA) network, which considers a two-path visual mechanism and realizes the binocular fusion based on parallax information. To better aggregate binocular features from different levels, a two-path feature aggregation structure, which simulates the bottom-up and top-down mechanism in human visual system (HVS), is proposed. It not only realizes the supplement of low-level detail information to high-level semantic in the bottom-up path, but also realizes the supplement of high-level semantic information to low-level detail in the top-down path. Simultaneously, because feature misalignment exists in binocular features of adjacent levels, a feature alignment module (FAM) based on deformable convolution is designed to integrate the binocular fusion features of adjacent levels. In addition, considering the importance role of parallax in guiding binocular fusion, a binocular fusion module (BFM) based on parallax attention mechanism, which is different with existing binocular fusion methods, is explicitly proposed to achieve the binocular fusion between the left and right view features. Extensive experiments are conducted on LIVE I, LIVE II, WIVC I and WIVC II databases to demonstrate the effectiveness of the proposed method.

A stimulus exposure of 50 ms elicits the uncanny valley effect

The uncanny valley (UV) effect captures the observation that artificial entities with near-human appearances tend to create feelings of eeriness. Researchers have proposed many hypotheses to explain the UV effect, but the visual processing mechanisms of the UV have yet to be fully understood. In the present study, we examined if the UV effect is as accessible in brief stimulus exposures compared to long stimulus exposures (Experiment 1). Forty-one participants, aged 21–31, rated each human-robot face presented for either a brief (50 ms) or long duration (3 s) in terms of attractiveness, eeriness, and humanness (UV indices) in a 7-point Likert scale. We found that brief and long exposures to stimuli generated a similar UV effect. This suggests that the UV effect is accessible at early visual processing. We then examined the effect of exposure duration on the categorisation of visual stimuli in Experiment 2. Thirty-three participants, aged 21–31, categorised faces as either human or robot in a two-alternative forced choice task. Their response accuracy and variance were recorded. We found that brief stimulus exposures generated significantly higher response variation and errors than the long exposure condition. This indicated that participants were more uncertain in categorising faces in the brief exposure condition due to insufficient time. Further comparisons between Experiment 1 and 2 revealed that the eeriest faces were not the hardest to categorise. Overall, these findings indicate (1) that both the UV effect and categorical uncertainty can be elicited through brief stimulus exposure, but (2) that categorical uncertainty is unlikely to cause the UV effect. These findings provide insights towards the perception of robotic faces and implications for the design of robots, androids, avatars, and artificial intelligence agents.

Research on extraction of carbon from coal gasification fine slag using hydrophobic-hydrophilic separation: Efficient separation, molecular dynamics simulation, and model optimization

Disposing of coal gasification fine slag (CGFS) in piles and landfills result in enormous resource wastage and potential environmental risks. The precondition for CGFS resource utilization is separating residual carbon and ash materials. However, froth flotation products are heavily entrained, and gravity separation is not targeted for fines. This research introduced Hydrophobic-Hydrophilic Separation (HHS) as a solution to achieve efficient separation of residual carbon and ash materials. We investigated the surface morphology, elemental composition, and functional group species of residual carbon and ash materials by various testing methods. Additionally, molecular dynamics simulations were employed to reveal the visualization mechanism of CGFS in HHS. Response surface methodology analyzed the effects of mixing time, stirring speed, and n-heptane dosage on HHS carbon/ash separation. Furthermore, the sequence and interaction relationships of factor effects were analyzed, and optimal parameter values were determined. The advantages of HHS over froth flotation and gravity separation were briefly discussed. Finally, research directions were proposed for the process design of HHS and resource utilization with HHS products. This research would provide the theoretical basis and technical support for the efficient separation of residual carbon and ash materials, the large-scale resource utilization of CGFS, and the high-value processing of HHS products, which was beneficial to clean production, sustainable development, and environmental management.

Exposure to environmentally relevant concentrations of Bisphenol-A linked to loss of visual lateralization in adult zebrafish (Danio rerio)

Weak, but environmentally relevant concentrations of contaminants can have subtle, yet important, impacts on organisms, which are often overlooked due to the lack of acute impacts and the timing of exposure. Thus, recognizing simple, non-invasive markers of contamination events is essential for early detection and addressing the effects of exposure to weak environmental contaminants. Here, we tested whether exposure to an environmentally relevant concentration of Bisphenol-A (BPA), a common and persistent contaminant in aquatic systems, affects the lateralization of adult zebrafish (Danio rerio), a widely used model organism in ecotoxicology. We found that 73.5% of adult zebrafish displayed a left-side bias when they approached a visual cue, but that those exposed to weak BPA (0.02 mg/L) for 7 days did not exhibit laterality. Only 47.1% displayed a left-side bias. We found no differences in activity level and visual sensitivity, motor and sensory mechanisms, that regulate lateralized responses and that were unaffected by weak BPA exposure. These findings indicate the reliability of laterality as a simple measure of contaminant exposure and for future studies of the detailed mechanisms underlying subtle and complex behavioral effects to pollutants.

A Design Language for Prototyping and Storyboarding Data-Driven Stories

Data-driven stories (DDS) are digital forms of storytelling that arrange data and visualizations to communicate a narrative of information to an audience. They have been growing rapidly over the past decades. As a result, a great degree of versatility appears in the forms of published DDS. The recent structures of DDS are more complex, respecting their arrangement, composition, features, and inner parts. In the current academic research, neither storytelling techniques nor any taxonomies suggest visual mechanisms to distinguish between different layouts, compositions, and arrangements. The lack of an expressive visual solution that integrates different parts of DDS under one structure prevents the authors from trying more alternative design paths in the story design process. In this proposed work, we unify all the constructing parts of DDS to define the narrative structure as a visually structured representation of the DDS narrative, which is formed and designed by their constructing elements. This solution proposes a design language consisting of a set of design rules that integrate the visual elements to represent the DDS narrative structure. Our evaluation of the audit process out of 100 DDS examples confirms that the design language is comprehensive, expressive, and versatile. Additionally, we developed DataStoryDesign, a system that incorporates this visual solution to facilitate prototyping and storyboarding DDS for a team of DDS authors. The preliminary result of the exploratory evaluation indicates that such a solution is effective in prototyping and storyboarding DDS. In addition, our findings confirmed that the existence of our design language improves the visual communication between different personas in the DDS production workflow.

Research on image classification leveraging deep convolutional neural networks and visual cognition

The field of image classification has experienced remarkable improvements with the advent of deep learning techniques, especially Deep Convolutional Neural Networks. The present study provides an extensive exploration of the junction where image classification based on Deep Convolutional Neural Networks meets human visual cognition. Utilizing the inherent ability of these networks to automatically learn hierarchical features from raw pixel data, this research examines their potential in classifying images from diverse complex datasets, emphasizing predominantly on the extensively utilized ImageNet dataset. The initial aspect of this study involves training and evaluating models based on Deep Convolutional Neural Networks on the ImageNet dataset, which comprises millions of labeled images spanning across thousands of categories. Well-established network architectures such as AlexNet, VGGNet, GoogLeNet, and ResNet are employed, and their performance in the challenging task of image classification is assessed. Rigorous experiments highlight the strengths and weaknesses of each model while emphasizing the prospects of transfer learning and fine-tuning. Following this, the interpretability of Deep Convolutional Neural Networks is explored by using visualization techniques to comprehend the learned feature representations. By visualizing activation maps and class-specific saliency maps, valuable insights are gained into the regions of interest that guide the decision-making of these models. Moreover, the correlation between the features extracted by these models and human visual attention mechanisms is examined to shed light on the focus of attention of the models. The study also addresses the difficulties that adversarial attacks, data bias, and generalization capabilities present to Deep Convolutional Neural Networks. Strategies to enhance the robustness and adaptability of the models across various domains are examined, linking these observations to human cognitive behavior.

Features Split and Aggregation Network for Camouflaged Object Detection.

Higher standards have been proposed for detection systems since camouflaged objects are not distinct enough, making it possible to ignore the difference between their background and foreground. In this paper, we present a new framework for Camouflaged Object Detection (COD) named FSANet, which consists mainly of three operations: spatial detail mining (SDM), cross-scale feature combination (CFC), and hierarchical feature aggregation decoder (HFAD). The framework simulates the three-stage detection process of the human visual mechanism when observing a camouflaged scene. Specifically, we have extracted five feature layers using the backbone and divided them into two parts with the second layer as the boundary. The SDM module simulates the human cursory inspection of the camouflaged objects to gather spatial details (such as edge, texture, etc.) and fuses the features to create a cursory impression. The CFC module is used to observe high-level features from various viewing angles and extracts the same features by thoroughly filtering features of various levels. We also design side-join multiplication in the CFC module to avoid detail distortion and use feature element-wise multiplication to filter out noise. Finally, we construct an HFAD module to deeply mine effective features from these two stages, direct the fusion of low-level features using high-level semantic knowledge, and improve the camouflage map using hierarchical cascade technology. Compared to the nineteen deep-learning-based methods in terms of seven widely used metrics, our proposed framework has clear advantages on four public COD datasets, demonstrating the effectiveness and superiority of our model.

Urban Mobility Analysis Using Geospatial Analysis

This project proposes a method to analyze the traffic and urban mobility of a locality with advanced visualization and understanding mechanisms. urban areas worldwide face escalating challenges in managing transportation systems efficiently. This research delves into the realm of urban mobility analysis, leveraging geospatial data to extract insightful patterns and trends. The project aims to contribute a comprehensive understanding of the dynamics governing urban transportation, enabling informed decision- making for city planning and infrastructure development. The methodology encompasses the collection of extensive geospatial datasets, including traffic flow, public transportation routes, and demographic information. Utilizing advanced geospatial analysis techniques, such as Geographic Information Systems(GIS) and spatial analytics, the study explores spatial-temporal patterns of traffic congestion, public transit usage, and pedestrian movement. Machine learning algorithms are employed to predict future mobility trends and assess the impact of potential interventions. The findings of this research offers valuable insights for urban planners, policymakers, and transportation authorities. By identifying key bottlenecks, optimizing transit routes, and understanding the interplay between land use and growing populations and foster sustainable urban development. This project not only contributes to academic understanding of urban mobility but also provides actionable recommendations for creating smarter and resilient cities. The integration of geospatial analysis techniques in urban planning emerges as a powerful tool to address the complexities of modern urban transportation systems. Keywords: informed decision-making, geospatial data, Geographic Information System(GIS), spatial analysis, sustainable urbandevelopment.

Visual Traditions in the Formation of the Iconographic Types of the Investiture and Triumph of Patriarch Joseph

The story of the patriarch Joseph is a very recurring theme in medieval visual artistic Christian tradition. Joseph, Jacob’s beloved son, is a prefiguration of Christ. The story in Genesis 41, 37–44 fosters the creation of two iconographic types: Joseph’s investiture and Joseph on the Pharaoh’s chariot. The narrative places patriarch Joseph in the court of the Pharaoh of Egypt. However, Christian visuality was created according to the iconic criteria for the representation of political power, contemporary to the configuration of both iconographic types. The aim of this paper is to study the visual mechanisms used in the iconic configuration of the iconographic types of Joseph’s investiture and Joseph on the Pharaoh’s chariot, when the monarch bestows upon Joseph the privilege of his trust. The iconographic analysis of some early and medieval examples of the artistic visuality of Joseph’s story, in Eastern and Western traditions, confirms that they refer back to late ancient and medieval Byzantine tradition. Likewise, it was detected that the resources used in the visual configuration of both iconographic types are linked to the conventionalised mechanisms of the symbolic construction of power.

A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design

With the proliferation of advanced visualization techniques in visual communication, enhancing digital image quality remains a persistent challenge. This study presents a sophisticated Convolutional Neural Network (CNN) model to optimize image processing. The model incorporates a multi-stage architecture attentive to biological visual pathways. Inter-subnetwork connections enable integrated feature learning, guided by adaptive weighting of luminance, color, orientation, and edge maps. Spatial and channel attention modules further enrich feature interplay. When evaluated on the LIVE 3D Phase dataset, the approach demonstrates marked improvements, with saliency maps closely mirroring human visual perception. Pearson Correlation Coefficient and Histogram Intersection metrics exceed conventional models, at 0.6486 and 0.7074 respectively. Testing across distortion types reveals strong agreement with subjective rankings, confirming the model's effectiveness. By combining automated feature extraction with insights from visual cortex mechanisms, this bio-inspired CNN framework significantly enhances image optimization and quality. The scalable approach provides a foundation for next-generation computer vision and machine learning applications.

IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures.

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet.

Parallel Multi-Path Network for Ocular Disease Detection Inspired by Visual Cognition Mechanism.

Various ocular diseases such as cataracts, glaucoma, and diabetic retinopathy have become several major factors causing non-congenital visual impairment, which seriously threatens people's vision health. The shortage of ophthalmic medical resources has brought huge obstacles to large-scale ocular disease screening. Therefore, it is necessary to use computer-aided diagnosis (CAD) technology to achieve large-scale screening and diagnosis of ocular diseases. In this work, inspired by the human visual cognition mechanism, we propose a parallel multi-path network for multiple ocular diseases detection, called PMP-OD, which integrates the detection of multiple common ocular diseases, including cataracts, glaucoma, diabetic retinopathy, and pathological myopia. The bottom-up features of the fundus image are extracted by a common convolutional module, the Low-level Feature Extraction module, which simulates the non-selective pathway. Simultaneously, the top-down vessel and other lesion features are extracted by the High-level Feature Extraction module that simulates the selective pathway. The retinal vessel and lesion features can be regarded as task-driven high-level semantic information in the physician's disease diagnosis process. Then, the features are fused by a feature fusion module based on the attention mechanism. Finally, the disease classifier gives prediction results according to the integrated multi-features. The experimental results indicate that our PMP-OD model outperforms other state-of-the-art (SOTA) models on an ocular disease dataset reconstructed from ODIR-5K, APTOS-2019, ORIGA-light, and Kaggle.

A visual pollination mechanism of a new specialized pollinating weevil-plant reciprocity system.

Pollinating flower-consuming mutualisms are considered exemplary models for studying coevolution due to their rarity. Visual cues are considered to have a major role in facilitating the evolution of floral patterns in these systems. We present a new specialized pollinating flower-consuming mutualism from the plant Wurfbainia villosa, which is a traditional Chinese herbal medicine, by a pollinating weevil, Xenysmoderes sp. In this study, We utilized monochrome plates for binary-choice tests to determine weevil color preferences, conducted behavioral choice experiments, using trackballs, photographed flowers and weevils, and employed blue sticky boards to attract weevils in the field. Tests were conducted using colorpreferring weevils in both indoor and outdoor field systems, and validation experiments were performed. Behavioral tests were conducted to investigate the role of the visual cues in the pollinator attraction of W. villosa, which is a selfcompatible insect-pollinated plant that relies primarily on the Xenysmoderes sp. weevil for pollination due to its specialized gynandrium-like structure. Behavioral tests demonstrated that a blue color wavelength of 480 nm and the blue color system, as along with the UV-style pattern of the flowers, particularly the parts with specialized gynandrium-like structures in the labellum, were significantly attractive to both male and female weevils. These results were further confirmed through the field blue sticky board trap method. These findings indicated that the interaction between W. villosa and Xenysmoderes sp. weevil was a novel symbiotic relationship involving pollinator flower consumption. Additionally, Wurfbainia villosa flowers developed specific visual cues of UV patterns and specialized structures that played a crucial role in attracting pollinators.

The guiding significance of consumer visual perception on the packaging design of cultural and creative products

Abstract The packaging of cultural and creative products plays a crucial role in enhancing their competitiveness, with exquisite packaging significantly contributing to meeting the personalized needs of consumers and stimulating their desire to purchase. This paper examines the visual perception factors involved in the packaging design of cultural and creative products using the visual attention mechanism and categorizes the design factors according to demand using the Kano model. The hierarchical analysis method is used to determine the consumer demand weight of each design factor. The packaging solutions for cultural and creative products are finalized, and the TOPSIS method is employed to assess each solution. The hierarchical analysis model achieves consistency ratios of 0.076, 0.016, 0.003, and 0.004 for the target layer Z and the criterion layers M, O, and A, respectively, all of which are less than 0.1, thereby passing the consistency test. Among the three designed packaging schemes for cultural and creative products, scheme 1 has the largest S value, which is 0.899. Therefore, scheme 1 is the best design scheme. The existing sample scheme has a S value of 0.013, which is smaller than that of the other three design schemes. The integrated Kano-AHP-TOPSIS packaging design path’s design schemes for cultural and creative products are more likely to meet consumers’ needs, demonstrating the path’s practical feasibility and the importance of consumer visual perception in shaping packaging design for these products.

Attention-Driven Memory Network for Online Visual Tracking.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

A collective perception model for neighbor selection in groups based on visual attention mechanisms

The mechanisms by which individuals visually perceive and select others within a group to interact with are of fundamental importance to various collective motion behaviors. However, they remain poorly understood theoretically, partially due to the challenges of modeling neurological perception systems. Here, the classic zoom-lens visual attention model is introduced into collective perception, leading to an attention-based model with only a single parameter. Numerical experiments reveal that the proposed model offers a unified mechanism for collective perception, as reflected in three aspects. First, this model describes both conspecific (e.g. flocking) and heterospecific (e.g. predator-prey) collective behaviors. Second, this model unifies the well-known topological and visibility models in the context of neighbor selection in conspecific groups. Third, in the context of prey selection in heterospecific groups, the model can simultaneously replicate the well-known confusion and oddity effects. These findings demonstrate the fundamental role of visual attention underlying a diverse array of collective motion behaviors.

EEGVision: Reconstructing vision from human brain signals

Abstract The intricate mechanisms elucidating the interplay between human visual perceptions and cognitive processes remain elusive. Exploring and reconstructing visual stimuli from cerebral signals could help us better understand the processes by which the human brain generates visual imagery. However, the inherent complexity and significant noise in brain signals limit current efforts to reconstruct visual stimuli, resulting in low-granularity images that miss details. To address these challenges, this paper proposes EEGVision, a comprehensive framework for generating high-quality images directly from brain signals. Leveraging the recent strides in multi-modal models within the realm of deep learning, it is now feasible to bridge the gap between EEG data and visual representation. This process starts with a time-frequency fusion encoder in EEGVision, which quickly pulls out cross-domain and robust features from EEG signals. We then design two parallel pipelines to align EEG embeddings with image features at both perceptual and semantic levels. The process uses a stable diffusion-trained image-to-image pipeline that combines coarse and fine-grained data to get high-quality images back from EEG data. Both quantitative and qualitative assessments affirm that EEGVision surpasses contemporary benchmarks. This network architecture holds promise for further applications in the domain of neuroscience, aiming to unravel the genesis of human visual perception mechanisms. All code is accessible via https://github.com/AvancierGuo/EEGVision.

The left-lateralized N170 for visual specialization in advanced L2 Chinese learners.

Visual word recognition is crucial for improving reading skills in second language learners (L2Ls). It is unclear whether L2Ls who are native speakers of languages that use alphabetic scripts can recognize Chinese characters at an early stage of visual processing and if their visual specialization can reach a level of word recognition comparable to that of native Chinese speakers. This study aims to uncover the visual specialization mechanism of Chinese L2Ls. A delayed-color matching task was carried out with participants who were Chinese first language speakers (L1Ss) and advanced Chinese L2Ls with Indonesian as their first language. The results of the event-related potentials (ERPs) indicated that L2Ls exhibited significant visual specialization with a predominant distribution of the left-lateralized N170, along with some activation in the right hemisphere. These findings suggest that the early processing stage of Chinese characters by advanced L2Ls is similar to that of adult native speakers, although it is still influenced by their first language and its writing system.

Solar photovoltaic cell model optimal parameter identification by using an improved chimp optimization algorithm

Identifying the parameters of solar photovoltaic (PV) cell models accurately and reliably is crucial for simulating, evaluating, and controlling PV systems. For this reason, we present an improved chimp optimization algorithm (IChOA) for the generation of precise and reliable solar PV cell models. As a new and improved version of the standard chimp optimization algorithm (ChOA), IChOA embeds two mutation rules in ChOA that include the elite opposition-based learning and visual search mechanism. The first rule is applied to strengthen global exploration capacity of ChOA, and the second one is utilized to enhance ChOA’s local exploitation ability (convergence accuracy). Based on the six benchmark test functions with different characteristics, the effectiveness of IChOA is evaluated by comparing to other five well-known optimization algorithms. The results suggest that IChOA offers superior performance over other competing algorithms. Finally, IChOA’s performance is confirmed through optimizing parameters for three widely employed mathematical models, specifically the single diode model, the double diode model, and the multi-cell PV modules. The findings prove the excellent performance of the suggested approach.

Information communication and aesthetic value of data visualization in the field of art and design

Abstract Data visualization has become a popular discipline in today’s society, and understanding this trend has become a hot topic in the field of design. This paper introduces the visual perception system of the human eye, combining graphic features and dynamic features to reveal the visualization mechanism of information dynamic conveying in the field of art design. Then, the quantitative model of visual communication in color and graphic texture in art design, respectively, and the art design image containing rich plants are taken as an example to realize the visual analysis of information communication in the field of art design. There is a significant negative correlation (p > 0.05) between the purity of the foreground image, the foreground image color gamut area, and the proportion of foreground image color matching. This means that the higher the purity of the foreground image, the smaller the color matching area, leading to more coordinated color matching and a higher aesthetic degree. Simultaneously, the alteration in the gray value of the image texture typically exhibits characteristics such as high frequency, small amplitude, and randomness. The use of visualization technology in art design, reasonable collocation of colors, and graphic aesthetics fully demonstrate its aesthetic value.