Mechanisms Of Visual Perception Research Articles

Detection Method of Molten Iron Flow Velocity at Blast Furnace Taphole Combining Visual Perception and Jet Mechanism

Detection Method of Molten Iron Flow Velocity at Blast Furnace Taphole Combining Visual Perception and Jet Mechanism

Underwater Image Enhancement based on Retinex Decomposition and Unsupervised Generative Adversarial Networks

Background:: Due to the difficulty of obtaining the real dataset of paired underwater images, it is urgent to build an unsupervised underwater image enhancement network. Objective:: To address the problem, a novel underwater image enhancement based on Retinex decomposition and Unsupervised Generative Adversarial Network (RUGAN) is proposed. Method:: A color correction module is proposed considering the different color distortions of underwater images. Further, considering the human visual perception mechanism, the RUGAN network, which is similar to U-Net, is constructed using the characteristics of underwater imaging and Retinex decomposition. Based on Retinex decomposition and the characteristics of underwater imaging, the RUGAN network similar to U-Net is constructed. The reflectance image and illumination image are obtained. The reflectance image with a better effect is taken as the enhancement result. Unlike the previous supervised methods, RUGAN adopts clear air images and distorted underwater images as training. RUGAN adopts the underwater image of the color correction module as pseudo-ground truth to achieve an unsupervised effect. Results:: The superiority of RUGAN network is further supported by extensive experiments that compared it with more methods. conclusion: The proposed RUGAN achieves better results both subjectively and objectively.

Brain Decoding over the MEG Signals Using Riemannian Approach and Machine Learning

Brain decoding is an emerging approach for understanding the face perception mechanism in the human brain. Face visual stimuli and perception mechanism are considered as a challenging ongoing research of the neuroscience field. In this study, face/scrambled face visual stimulations were implemented over the sixteen participants to be decoded the face or scrambled face classification using machine learning (ML) algorithms via magnetoencephalography (MEG) signals. This noninvasive and high spatial/temporal resolution signal is a neurophysiological technique which measures the magnetic fields generated by the neuronal activity of the brain. The Riemannian approach was used as a highly promising feature extraction technique. Then Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) were employed as deep learning algorithms, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) were implemented as shallow algorithms. The improved classification performances are very encouraging, especially for deep learning algorithms. The LSTM and GRU have achieved 92.99% and 91.66% accuracy and 0.977 and 0.973 of the area under the curve (AUC) scores, respectively. Moreover, CNN has yielded 90.62% accuracy. As our best knowledge, the improved outcomes and the usage of the deep learning on the MEG dataset signals from 16 participants are critical to expand the literature of brain decoding after visual stimuli. And this study is the first attempt with these methods in systematic comparison. Moreover, MEG-based Brain-Computer Interface (BCI) approaches may also be implemented for Internet of Things (IoT) applications, including biometric authentication, thanks to the specific stimuli of individual’s brainwaves.

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95. Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

Wafer defect recognition method based on multi-scale feature fusion.

Wafer defect recognition is an important process of chip manufacturing. As different process flows can lead to different defect types, the correct identification of defect patterns is important for recognizing manufacturing problems and fixing them in good time. To achieve high precision identification of wafer defects and improve the quality and production yield of wafers, this paper proposes a Multi-Feature Fusion Perceptual Network (MFFP-Net) inspired by human visual perception mechanisms. The MFFP-Net can process information at various scales and then aggregate it so that the next stage can abstract features from the different scales simultaneously. The proposed feature fusion module can obtain higher fine-grained and richer features to capture key texture details and avoid important information loss. The final experiments show that MFFP-Net achieves good generalized ability and state-of-the-art results on real-world dataset WM-811K, with an accuracy of 96.71%, this provides an effective way for the chip manufacturing industry to improve the yield rate.

Different aspects of visual perception are important for 12-year social functioning depending on gestational age.

Perceptual mechanisms in social functioning might promote interventions. We investigated relations between visual perception and social functioning, in preterm children. A prospective preterm cohort born in Uppsala County, Sweden, in 2004-2007 and 49 full-term controls were examined at 12 years. Aspects of visual perception, including static shapes, emotions, and time to detect biological motion, were related to social functioning and visual acuity. The preterm group comprised 25 extremely preterm children, EPT, born below 28 gestational weeks and 53 children born between 28-31 weeks. Preterm children had difficulties in perception of static shapes (p=0.004) and biological motion, p<0.001), but not in emotion perception, compared to controls. In the EPT children, poorer shape perception and lower scores on emotion perception were associated with more social problems (p=0.008) and lower visual acuity (p=0.004). Shape perception explained more variance in social functioning than emotion perception. In controls, fewer social problems were linked to faster biological motion perception (p=0.04). Static shape and biological motion perception was affected in the preterm groups. Biological motion perception was relevant for social functioning in full-term children. In EPT children, only shape perception was linked to social functioning, suggesting differential visual perception mechanisms for social deficits.

Flipping the world upside down: Using eye tracking in virtual reality to study visual search in inverted scenes.

Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on twodimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior.

Models of Visual Perception in the Turkish Language Worldview

This article is devoted to the study of the mechanism of modeling visual perception in the Turkish language picture of the world. It should be noted that when modeling visual perception, a number of factors play a role, under the influence of which different models are formed. This explains the structure of a typical visual perception situation, which is presented as the basis of modeling.

A Mathematical Model for the Action Spectrum of Steady-State Pupil Size in Photopic Vision with Insight into Healthful Lighting

The pupillary light reflex, which has been seen as an important noninvasive and objective indicator of autonomic nervous system function, can be used for evaluating the impact of different lighting conditions in buildings on circadian behaviors, assessing ipRGC function in healthy and diseased retinas, and explaining luminance adaptation. However, the mechanism by which the intrinsic and extrinsic signals of ipRGCs regulate the steady-state pupil size under continuous lighting stimuli is still not clearly understood after decades of exploration. This paper presents a new experimental protocol with a large hemisphere LED screen as the stimulation device, allowing for a more realistic and comprehensive study in architectural spaces, which can potentially inform the design of lighting systems in buildings that promote healthy vision and comfort. Results reveal that both intrinsic and extrinsic signals participated in the process of regulating pupil size under continuous lighting conditions. Based on the findings, a new mathematical model was further proposed to calculate the contribution of these two signal sources to the changing intensity of melanopic radiance. The research outcomes also provide new insight into the mechanism of visual perception and adaptation and the nonvisual effect of eyes under different light conditions. Results suggest that the contribution of extrinsic signals may have been underestimated in previous studies since the extrinsic signal increases with reducing intensity in photopic conditions with lower melanopic radiance.

Leibniz and Kant

<i>Leibniz and Kant</i>

Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal Sampling.

Adaptive sampling that exploits the spatiotemporal redundancy in videos is critical for always-on action recognition on wearable devices with limited computing and battery resources. The commonly used fixed sampling strategy is not context-aware and may under-sample the visual content, and thus adversely impacts both computation efficiency and accuracy. Inspired by the concepts of foveal vision and pre-attentive processing from the human visual perception mechanism, we introduce a novel adaptive spatiotemporal sampling scheme for efficient action recognition. Our system pre-scans the global scene context at low-resolution and decides to skip or request high-resolution features at salient regions for further processing. We validate the system on EPIC-KITCHENS and UCF-101 (split-1) datasets for action recognition, and show that our proposed approach can greatly speed up inference with a tolerable loss of accuracy compared with those from state-of-the-art baselines. Source code is available in https://github.com/knmac/adaptive_spatiotemporal.

Mechanisms of human dynamic visual perception revealed by sequential deep neural networks

Mechanisms of human dynamic visual perception revealed by sequential deep neural networks

Attentional capture mediates the emergence and suppression of intrusive memories.

Intrusive memories hijack consciousness and their control may lead to forgetting. However, the contribution of reflexive attention to qualifying a memory signal as interfering is unknown. We used machine learning to decode the brain's electrical activity and pinpoint the otherwise hidden emergence of intrusive memories reported during a memory suppression task. Importantly, the algorithm was trained on an independent attentional model of visual activity, mimicking either the abrupt and interfering appearance of visual scenes into conscious awareness or their deliberate exploration. Intrusion of memories into conscious awareness were decoded above chance. The decoding accuracy increased when the algorithm was trained using a model of reflexive attention. Conscious detection of intrusive activity decoded from the brain signal was central to the future silencing of suppressed memories and laterforgetting. Unwanted memories require the reflexive orienting of attention and access to consciousness to be suppressed effectively by inhibitory control.

Restructuring of the Optokinetic Reflex during Metamorphosis in Pelobates fuscus Laur.

The restructuring of the gaze stabilization system in Pelobates fuscus was investigated by quantitative analysis of the optomotor response using video imaging. Gaze stabilization is an important component in the system of neural mechanisms of visual depth perception. It was shown that the optomotor response in aquatic tadpoles of P. fuscus is similar to that of fish (movement of the animal in the direction of the visual background movement and eye nystagmus consisting of a fast and a slow phase). During metamorphosis (transition from the aquatic to the terrestrial lifestyle), froglets of P. fuscus responded to the movement of the visual background by eyes and head movements. One year after metamorphosis, P. fuscus responded to movement of the visual background as adult Anura: only by head movements (a slow and a fast phase), while eye movements were absent. Possible causes of the loss of active eye movements by Anura amphibians in the process of evolution are discussed.

Event-Related Potential Assessment of Visual Perception Abnormality in Patients With Obstructive Sleep Apnea: A Preliminary Study

This study investigated the effect of obstructive sleep apnea (OSA) on the neural mechanism of visual perception. A preliminary case-control study was conducted. Seventeen patients with moderate to severe OSA in the sleep center of Civil Aviation General Hospital and 20 healthy controls matched for age, sex, and education were recruited. The participants accepted the perceptual contour integration task, compared the differences in behavioral indicators between the two groups, and compared the differences in electroencephalography (EEG) data between the two groups through event-related potential (ERP) technology. The groups did not differ significantly in age and gender, but they differed significantly in body mass index (BMI) (OSA higher than healthy controls, p < 0.05). The groups were not statistically different in terms of sleep structure and total sleep time. AHI, sleep efficiency, and minimal SaO2 value in the OSA group were significantly different from those of the control group. The results showed that the average reaction time of the OSA group was significantly longer than that of the healthy control group in the contour integration task. There was no significant difference in the accuracy rate. The results of EEG showed that the amplitudes of N100 of the OSA group were significantly higher than those of the control group at O1, Oz, and O2 electrodes. There was no significant difference in latency between the two groups. At the FCz electrode, the amplitudes of N200 of the OSA group were significantly higher than those of the control group. Therefore, we concluded that in the early stage of the perceptual integration task, although the neural response activity of patients with moderate and severe OSA was not accelerated, they need to call on more psychological resources, activate more neurons in the contour global recognition processing stage, and the compensatory effect of frontal lobe appeared in the stage of visual perception.

Assessment and Semantic Categorization of Fabric Visual Texture Preferences

Abstract The texture of a fabric can be perceived by the haptic and visual senses. Visual texture can be defined as a visual quality of a surface. It is an important phenomenon because it can be significant in many fields, such as textile design and e-commerce. At the same time, when we consider the semantics of the word, it is important to take into account that there are a variety of manifestations of fabrics (e.g., woven, knitted, etc.). The mechanism of visual texture perception of fabrics was investigated by measuring visual evaluation values. In our experiment, 12 textile samples with different surface textures are evaluated using thirty-four adjectives (Kansei words). For each visual texture, the adjectives with the highest mean ratings are extracted and analyzed. By using Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), we aimed to discover and determine preferences for the visual texture of fabrics. The result is a semantic explanation of fabric texture with the adjectives proposed, which can help customers to evaluate the quality of the textile.

An Integrated Neuroimaging Approach to Inform Transcranial Electrical Stimulation Targeting in Visual Hallucinations.

For decades, noninvasive brain stimulation (NIBS), such as transcranial electrical stimulation (tES), has been used to directly modulate human brain mechanisms of visual perception, setting the groundwork for the development of novel circuit-based therapies. While the field of NIBS has grown considerably over recent years, few studies have used these technologies to treat visual hallucinations (VH). Here, we review the NIBS-VH literature and find mixed results due to shortcomings that may potentially be addressed with a unique multimodal neuroimaging-NIBS approach. We highlight methodological advances in NIBS research that have provided researchers with more precise anatomical measurements that may improve our ability to influence brain activity. Specifically, we propose a methodology that combines neuroimaging advances, clinical neuroscience developments such as the identification of brain regions causally involved in VH, and personalized NIBS approaches that improve anatomical targeting. This methodology may enable us to reconcile existing discrepancies in tES-VH research and pave the way for more effective, VH-specific protocols for treating a number of neuropsychiatric disorders with VH as a core symptom.

P574. The Psychosis Human Connectome Project: Studies of Visual Processing

People with psychosis experience anomalous visual percepts, but the neural basis of visual dysfunction remains unclear. Vision offers a strong translational bridge between basic neuroscience in animal models and studies in humans, permitting strong inferences about the neurophysiological basis of perceptual anomalies. The goal of the current study was to use behavioral and brain imaging measures to elucidate the neural mechanisms of abnormal visual perception among people with psychosis and their relatives.

Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment

In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction. In particular, we evaluate the quality of a video by learning effective feature representations in spatial-temporal domain. In the spatial domain, to tackle the resolution and content variations, we impose the Gaussian distribution constraints on the quality features. The unified distribution can significantly reduce the domain gap between different video samples, resulting in more generalized quality feature representation. Along the temporal dimension, inspired by the mechanism of visual perception, we propose a pyramid temporal aggregation module by involving the short-term and long-term memory to aggregate the frame-level quality. Experiments show that our method outperforms the state-of-the-art methods on cross-dataset settings, and achieves comparable performance on intra-dataset configurations, demonstrating the high-generalization capability of the proposed method. The codes are released at <i><uri>https://github.com/Baoliang93/GSTVQA</uri></i>

Region-aware network: Model human’s Top-Down visual perception mechanism for crowd counting

Background noise and scale variation are common problems that have been long recognized in crowd counting. Humans glance at a crowd image and instantly know the approximate number of human and where they are through attention the crowd regions and the congestion degree of crowd regions with a global receptive field. Hence, in this paper, we propose a novel feedback network with Region-Aware block called RANet by modeling human’s Top-Down visual perception mechanism. Firstly, we introduce a feedback architecture to generate priority maps that provide prior about candidate crowd regions in input images. The prior enables the RANet pay more attention to crowd regions. Then we design Region-Aware block that could adaptively encode the contextual information into input images through global receptive field. More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity. The relevance matrix obtained would be utilized to build global relationships between pixels. Our method outperforms state-of-the-art crowd counting methods on several public datasets.