Sight Research Articles

The Domains of Aesthetics and Perception Theories: A Review Relevant to Practice-based Doctoral Theses in the Visual Arts

Abstract Every doctoral thesis requires contextualization within its specific discipline's theoretical bases. For a visual arts practice-based thesis, the relevant bases include those of aesthetics and visual perception. This article reviews a Western history of the domain of visual aesthetic theory, addressing both the analytical philosophical efforts to define art and the continental approaches, which construe art as social construction. It then reviews a third, normative stance that foregrounds cognitive value before definition or sociological context—an aesthetic cognitivist position, art practice as a means of acquiring and sharing experiential knowledge and understanding. Contemporary concerns about environmental aesthetics and somaesthetics are also addressed in the Introduction. Aesthetic theories are related to visual perception theories as an aid to fulfilling the scholarly requirements for a practice-based thesis.

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.

VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision.

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and - as usual - before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities.

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Objective. This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. Approach. DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. Main results. Experiments were conducted at 2× , 4× , and 8× magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2× , 4× , and 8× ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR’s SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. Significance. This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

Progressive Adapting and Pruning: Domain-Incremental Learning for Saliency Prediction

Saliency prediction (SAP) plays a crucial role in simulating the visual perception function of human beings. In practical situations, humans can quickly grasp saliency extraction in new image domains. However, current SAP methods mainly concentrate on training models in single domains, which do not effectively handle diverse content and styles present in real-world images. As a result, it would be of great significance if SAP models could efficiently adjust to new image domains. To this end, this article aims to design SAP models that can imitate the incremental learning ability of human beings on multiple image domains and name domain-incremental saliency prediction (DISAP). To make a tradeoff between preventing the forgetting of historical domains and achieving high performance on new domains, we propose a progressively updated domain incremental encoder. This encoder consists of a domain-sharing branch and a domain-specific branch. The domain-sharing branch includes a feature selection mechanism to preserve crucial parameters after fine-tuning the model on each current domain. The remaining parameters are reserved to absorb knowledge from future domains. Furthermore, to capture the unique characteristics of each domain with relatively low computational overhead, we introduce a lightweight design to construct the domain-specific branch, enabling effective adaptation to new domains. Extensive experiments are conducted on multiple domain-incremental learning settings formed by four saliency prediction datasets, including Salicon, MIT1003, the art subset of CAT2000, and WebSal. The results demonstrate that our method outperforms existing methods significantly. The code is available at https://github.com/KaIi-github/DIL4SAP .

What does visual snow look like? Quantification by matching a simulation.

The primary symptom of visual snow syndrome (VSS) is the unremitting perception of small, flickering dots covering the visual field. VSS is a serious but poorly understood condition that can interfere with daily tasks. Several studies have provided qualitative data about the appearance of visual snow, but methods to quantify the symptom are lacking. Here, we developed a task in which participants with VSS adjusted parameters of simulated visual snow on a computer monitor until the simulation matched their internal visual snow. On each trial, participants (n = 31 with VSS) modified the size, density, update speed, and contrast of the simulation. Participants' settings were highly reliable across trials (intraclass correlation coefficients > 0.89), and they reported that the task was effective at stimulating their visual snow. On average, visual snow was very small (less than 2 arcmin in diameter), updated quickly (mean temporal frequency = 18.2 Hz), had low density (mean snow elements vs. background = 2.87%), and had low contrast (average root mean square contrast = 2.56%). Our task provided a quantitative assessment of visual snow percepts, which may help individuals with VSS communicate their experience to others, facilitate assessment of treatment efficacy, and further our understanding of the trajectory of symptoms, as well as the neural origins of VSS.

The Effect of Oculomotor Exercises With Gaze Stabilization on Hand-Eye Coordination and Reaction Time in Table Tennis Athletes

The purpose of this study was to determine the effect of a 6 week oculomotor exercise program on hand-eye coordination, reaction time, visual perception and dynamic visual acuity with table tennis athletes. 50 table tennis players included in the study were randomly divided in to oculomotor exercise (OMEG, n=25) and control (CG, n=25) groups. While the two groups continued their routine tennis training programs, oculomotor exercises with gaze stabilization were applied to OMEG twice a day for 6 weeks via the WhatsApp application. Plate tapping test was used to evaluate the eye-hand coordination of the participants, reaction time test (number of hits and average reaction time) using the Blazepod device was used to evaluate reaction times, and forms A and B of the Trail Making Test (TMT-A ve TMT-B) were used to evaluate visual perception. Dynamic visual acuity test with powerpoint was used to evaluate the dynamic visual acuity of the participants. The paired t-test and two-sample t-test was executed to examine within and between the groups. The OMEG had significant improvements in hand-eye coordination, visual perception, reaction time and dynamic visual acuity (p

Meta-analysis of experimental factors influencing single-pulse TMS effects on the early visual cortex.

Single-pulse transcranial magnetic stimulation (spTMS) applied to the Early Visual Cortex (EVC) has demonstrated the ability to suppress the perception on visual targets, akin to the effect of visual masking. However, the reported spTMS suppression effects across various studies have displayed inconsistency. We aim to test if the heterogeneity of the spTMS effects can be attributable to variations in experimental factors. We conducted a meta-analysis using data collected from the PubMed and Web of Science databases spanning from 1995 to March 2024. The meta-analysis encompassed a total of 40 independent experiments drawn from 33 original articles. The findings unveiled an overall significant spTMS suppression effect on visual perception. Nevertheless, there existed substantial heterogeneity among the experiments. Univariate analysis elucidated that the spTMS effects could be significantly influenced by TMS intensity, visual angle of the stimulus, coil type, and TMS stimulators from different manufacturers. Reliable spTMS suppression effects were observed within the time windows of -80 to 0 ms and 50 to 150 ms. Multivariate linear regression analyses, which included SOA, TMS intensity, visual angle of the stimulus, and coil type, identified SOA as the key factor influencing the spTMS effects. Within the 50 to 150 ms time window, optimal SOAs were identified as 112 ms and 98 ms for objective and subjective performance, respectively. Collectively, multiple experimental factors accounted for 22.9% (r = 0.3353) and 39.9% (r = 0.3724) of the variance in objective and subjective performance, respectively. Comparing univariate and multivariate analyses, it was evident that experimental factors had different impacts on objective performance and subjective performance. The present study provided quantitative recommendations for future experiments involving the spTMS effects on visual targets, offering guidance on how to configure experimental factors to achieve the optimal masking effect.

MSMHSA-DeepLab V3+: An Effective Multi-Scale, Multi-Head Self-Attention Network for Dual-Modality Cardiac Medical Image Segmentation.

The automatic segmentation of cardiac computed tomography (CT) and magnetic resonance imaging (MRI) plays a pivotal role in the prevention and treatment of cardiovascular diseases. In this study, we propose an efficient network based on the multi-scale, multi-head self-attention (MSMHSA) mechanism. The incorporation of this mechanism enables us to achieve larger receptive fields, facilitating the accurate segmentation of whole heart structures in both CT and MRI images. Within this network, features extracted from the shallow feature extraction network undergo a MHSA mechanism that closely aligns with human vision, resulting in the extraction of contextual semantic information more comprehensively and accurately. To improve the precision of cardiac substructure segmentation across varying sizes, our proposed method introduces three MHSA networks at distinct scales. This approach allows for fine-tuning the accuracy of micro-object segmentation by adapting the size of the segmented images. The efficacy of our method is rigorously validated on the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset, demonstrating competitive results and the accurate segmentation of seven cardiac substructures in both cardiac CT and MRI images. Through comparative experiments with advanced transformer-based models, our study provides compelling evidence that despite the remarkable achievements of transformer-based models, the fusion of CNN models and self-attention remains a simple yet highly effective approach for dual-modality whole heart segmentation.

Effects of looming audio on transcranial magnetic stimulation-induced phosphene perception.

Looming sounds are known to influence visual function in the brain, even as early as the primary visual cortex. However, despite evidence that looming sounds have a larger impact on cortical excitability than stationary sounds, the influence of varying looming strengths on visual ability remains unclear. Here, we aim to understand how these signals influence low-level visual function. Fourteen healthy undergraduate students participated. They were blindfolded and received transcranial magnetic stimulation (TMS) to the primary visual cortex following auditory stimulation with different strength looming sounds. Participants reported whether they perceived a phosphene, or an illusory visual percept, following TMS stimulation. We hypothesized that rates of phosphene activity would increase with increasing levels of looming strength. A linear mixed-effect model showed that phosphene activity was significantly higher at higher strength of looming (F(1, 69) = 5.33, p = .024) and at higher TMS pulse strength (F(1, 18) = 4.71, p = .043). However, there was also a significant interaction between looming strength and pulse strength (F(1, 69) = 4.33, p = .041). At lower levels of TMS strength, phosphene rate increased with looming strength, while at higher levels of TMS strength the effect was reversed. These results suggest a complex relationship between looming strength and cortical activity, potentially reflecting the mixed contribution of total auditory energy and the rate of changes. This work will enhance our ability to predict audiovisual interactions and may help improve auditory warning systems designed to capture visual attention.

Peripuberty is a sensitive period for prefrontal parvalbumin interneuron activity to impact adult cognitive flexibility.

Developmental windows in which experiences can elicit long-lasting effects on brain circuitry and behavior are called 'sensitive periods' and reflect a state of heightened plasticity. The classic example of a sensitive period comes from studies of sensory systems, like the visual system, where early visual experience is required for normal wiring of primary visual cortex and proper visual functioning. At a mechanistic level, loss of incoming visual input results in a decrease in activity in thalamocortical neurons representing the affected eye, resulting in an activity-dependent reduction in the representation of those inputs in the visual cortex and loss of visual perception in that eye. While associative cortical regions like the medial prefrontal cortex (mPFC) do not receive direct sensory input, recent findings demonstrate that changes in activity levels experienced by this region during defined windows in early development may also result in long-lasting changes in prefrontal cortical circuitry, network function and behavior. For example, we recently demonstrated that decreasing the activity of mPFC parvalbumin-expressing (PV) interneurons during a period of time encompassing peripuberty (postnatal day P14) to adolescence (P50) led to a long-lasting decrease in their functional inhibition of pyramidal cells, as well as impairments in cognitive flexibility. While the effects of manipulating mPFC PV interneuron activity were selective to development, and not adulthood, the exact timing of the sensitive period for this manipulation remains unknown. To refine the sensitive period in which inhibiting mPFC PV cell activity can lead to persist effects on prefrontal functioning we used a chemogenetic approach to restrict our inhibition of mPFC PV activity to two distinct windows: 1) peripuberty (P14-P32) and 2) early adolescence (P33-P50). We then investigated adult behavior after P90. In parallel, we performed histological analysis of molecular markers associated with sensitive period onset and offset in visual cortex, to define the onset and offset of peak sensitive period plasticity in the mPFC. We found that inhibition of mPFC PV interneurons in peripuberty (P14-P32), but not adolescence (P33-P50), led to an impairment in set shifting behavior in adulthood manifest as an increase in trials to reach criterion performance and errors. Consistent with a pubertal onset of sensitive period plasticity in the PFC, we found histological markers of sensitive period onset and offset also demarcated P14 and P35, respectively. The time course of expression of these markers was similar in visual cortex. Both lines of research converge on the peripubertal period (P14-32) as one of heightened sensitive period plasticity in the mPFC. Further, our direct comparison of markers of sensitive period plasticity across the prefrontal and visual cortex suggests a similar time course of expression, challenging the notion that sensitive periods occur hierarchically. Together, these findings extend our knowledge about the nature and timing of sensitive period plasticity in the developing mPFC.

Inattentional aftereffects: The role of attention on the strength of the motion aftereffect.

The way that attention affects the processing of visual information is one of the most intriguing fields in the study of visual perception. One way to examine this interaction is by studying the way perceptual aftereffects are modulated by attention. In the present study, we have manipulated attention during adaptation to translational motion generated by coherently moving random dots, in order to investigate the effect of the distraction of attention on the strength of the peripheral dynamic motion aftereffect (MAE). A foveal rapid serial visual presentation task (RSVP) of varying difficulty was introduced during the adaptation period while the adaptation and test stimuli were presented peripherally. Furthermore, to examine the interaction between the physical characteristics of the stimulus and attention, we have manipulated the motion coherence level of the adaptation stimuli. Our results suggested that the removal of attention through an irrelevant task modulated the MAE's magnitude moderately and that such an effect depends on the stimulus strength. We also showed that the MAE still persists with subthreshold and unattended stimuli, suggesting that perhaps attention is not required for the complete development of the MAE.

Natural scenes reveal diverse representations of 2D and 3D body pose in the human brain

Human pose, defined as the spatial relationships between body parts, carries instrumental information supporting the understanding of motion and action of a person. A substantial body of previous work has identified cortical areas responsive to images of bodies and different body parts. However, the neural basis underlying the visual perception of body part relationships has received less attention. To broaden our understanding of body perception, we analyzed high-resolution fMRI responses to a wide range of poses from over 4,000 complex natural scenes. Using ground-truth annotations and an application of three-dimensional (3D) pose reconstruction algorithms, we compared similarity patterns of cortical activity with similarity patterns built from human pose models with different levels of depth availability and viewpoint dependency. Targeting the challenge of explaining variance in complex natural image responses with interpretable models, we achieved statistically significant correlations between pose models and cortical activity patterns (though performance levels are substantially lower than the noise ceiling). We found that the 3D view-independent pose model, compared with two-dimensional models, better captures the activation from distinct cortical areas, including the right posterior superior temporal sulcus (pSTS). These areas, together with other pose-selective regions in the LOTC, form a broader, distributed cortical network with greater view-tolerance in more anterior patches. We interpret these findings in light of the computational complexity of natural body images, the wide range of visual tasks supported by pose structures, and possible shared principles for view-invariant processing between articulated objects and ordinary, rigid objects.

Autistic adults exhibit typical sensitivity to changes in interpersonal distance.

The visual processing differences seen in autism often impede individuals' visual perception of the social world. In particular, many autistic people exhibit poor face recognition. Here, we sought to determine whether autistic adults also show impaired perception of dyadic social interactions-a class of stimulus thought to engage face-like visual processing. Our focus was the perception of interpersonal distance. Participants completed distance change detection tasks, in which they had to make perceptual decisions about the distance between two actors. On half of the trials, participants judged whether the actors moved closer together; on the other half, whether they moved further apart. In a nonsocial control task, participants made similar judgments about two grandfather clocks. We also assessed participants' face recognition ability using standardized measures. The autistic and nonautistic observers showed similar levels of perceptual sensitivity to changes in interpersonal distance when viewing social interactions. As expected, however, the autistic observers showed clear signs of impaired face recognition. Despite putative similarities between the visual processing of faces and dyadic social interactions, our results suggest that these two facets of social vision may dissociate.

A Wearable Visually Impaired Assistive System Based on Semantic Vision SLAM for Grasping Operation.

Because of the absence of visual perception, visually impaired individuals encounter various difficulties in their daily lives. This paper proposes a visual aid system designed specifically for visually impaired individuals, aiming to assist and guide them in grasping target objects within a tabletop environment. The system employs a visual perception module that incorporates a semantic visual SLAM algorithm, achieved through the fusion of ORB-SLAM2 and YOLO V5s, enabling the construction of a semantic map of the environment. In the human-machine cooperation module, a depth camera is integrated into a wearable device worn on the hand, while a vibration array feedback device conveys directional information of the target to visually impaired individuals for tactile interaction. To enhance the system's versatility, a Dobot Magician manipulator is also employed to aid visually impaired individuals in grasping tasks. The performance of the semantic visual SLAM algorithm in terms of localization and semantic mapping was thoroughly tested. Additionally, several experiments were conducted to simulate visually impaired individuals' interactions in grasping target objects, effectively verifying the feasibility and effectiveness of the proposed system. Overall, this system demonstrates its capability to assist and guide visually impaired individuals in perceiving and acquiring target objects.

IoT Based Smart Palm Oil Seed Segregator using RGB Color Sensor

The manual seed grading process through human vision is tedious and time-consuming due to the tendency for errors and inconsistencies. Shell residues also failed to be properly segregated and required a huge amount of human energy as they were unable to be detected automatically once the waste container was full. The main objectives of this research are to develop an automated palm seed grading and sorting system to increase the seed’s sorting quality. The system can segregate palm oil and shell residues automatically to ease kernel recycling in the future. In addition, the system can implement the waste bins’ level detection, which can notify the user via the Blynk application, reducing the time and manpower required. To develop this system, sensor comparisons are done to determine the best-performing sensor to be used in the operation. The result shows that the RGB color sensor is the best color detecting sensor with an increment accuracy of 30.525%. As for the smart bin for shell residue, system response became 62.96% faster with the use of an ultrasonic sensor. The RGB sensor detects seed with readings <165 color concentration as freshly ripe and readings >165 color concentration as overripe. The janitor can be notified in real-time when the bins for oil and shell residues are full through the Blynk application using the WiFi module (ESP8266). The system has 100% accuracy, which was tested using the confusion matrix formula for both seed categories.

MISP-Fuse: A progressive fusion network guided by Multi-Information supervision

The fusion of infrared and visible images aims to synthesize an image that combines the advantageous characteristics of both image sources. Nevertheless, existing image fusion algorithms often struggle to extract relevant feature information from the source images and find it challenging to balance the significance of different modality information within the source images. This leads to suboptimal fusion results and inadequately serve advanced downstream visual tasks. In order to confront this challenge, a novel image fusion algorithm, denoted as Multiple Information Supervised Progressive Fusion Network (MISP-Fuse), has been proposed. Specifically, MISP-Fuse employs an innovative and robust multi-stage encoder-decoder network called Full Scale Feature Residual network (FSFR) to extract spatial context and detailed feature information corresponding to infrared and visible images. Within this encoder-decoder network, source image feature information is progressively extracted at different stages, which are recombined at multiple scales and distilled for crucial information. Finally, the fusion image is generated through a spatial localization module named as Spatial Localization Network (SLNet). MISP-Fuse incorporates a multi-information supervision mechanism to establish a linkage between different modality information in the infrared and visible source images. It ensures that the resulting fused image not only aligns with human visual perception but also effectively serves advanced downstream visual tasks. The comparative experiments utilizing diverse image fusion benchmark datasets has been conducted. In comparison to other algorithms, MISP-Fuse demonstrated significant enhancements in comprehensive image fusion metrics, including Average Gradient (AG), Sum of Correlated Differences (SCD), and Correlation Coefficient (CC).

EndoSRR: a comprehensive multi-stage approach for endoscopic specular reflection removal.

Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal. We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting. In the reflection detection stage, we adapt and fine-tune the segment anything model (SAM) using a weakly labeled dataset, achieving an accurate reflection mask. For reflective region inpainting, we employ LaMa, a fast Fourier convolution-based model trained on a 4.5M-image dataset, enabling effective inpainting of arbitrarily shaped reflection regions. Lastly, we introduce an iterative optimization strategy for dual pre-trained models to refine the results of specular reflection removal, named DPMIO. Utilizing the SCARED-2019 dataset, our approach surpasses state-of-the-art methods in both qualitative and quantitative evaluations. Qualitatively, our method excels in accurately detecting reflective regions, yielding more natural and realistic inpainting results. Quantitatively, our method demonstrates superior performance in both segmentation evaluation metrics (IoU, E-measure, etc.) and image inpainting evaluation metrics (PSNR, SSIM, etc.). The experimental results underscore the significance of proficient endoscopic specular reflection removal for enhancing visual perception and downstream tasks. The methodology and results presented in this study are poised to catalyze advancements in specular reflection removal, thereby augmenting the accuracy and safety of minimally invasive surgery.

The Effects of Physical Activity Interventions on Children's Perception: A Systematic Review and Meta-Analysis.

Perception is an essential component of children's psychological development, which is foundational to children's ability to understand and adapt to their external environment. Perception is also a crucial tool for understand and navigating one's surroundings, enabling children to identify objects and react appropriately to settings or situations. Substantial evidence indicates that engaging in physical activity is beneficial for the development of children's perceptual abilities, as the two are closely intertwined. Still, more research is necessary to gain a full understanding of the impact of physical activity on children's perception. To further identify and quantify the effects of physical activity on a number of specific perceptions in children. Systematic review and meta-analysis. Searches were performed using five online databases (i.e., PubMed, SPORTDiscus, PsycINFO, Web of Science, and Cochrane Library) for articles published up to and including June 2023 to identify eligible citations. A total of 12 randomized controlled trials, encompassing 1,761 children under the age of 12, were analyzed. Overall, physical activity as an intervention showed a notable effect on the development of children's perceptions. The meta-analysis indicated that participating in physical activity for 30 minutes around, daily, had a greater impact on children's visual perception and executive functioning than on their motor perception, body perception, and global self-worth (SMD = 1.33, 95% CI: 0.75, 1.91, p < 0.001). The effects of physical activity on children's perception performance varied by participant characteristics, with physical activity having better effects on body perception and overall self-worth in children who were obese or overweight. Furthermore, physical activity can also enhance executive function and attention in children with developmental coordination disorders. The effects of physical activity on children's perception performance varied according to the intervention time, with different activity durations resulting in different perception performances. Therefore, parents and educators must prioritize an appropriate length of physical activity time for children to ensure their optimal growth and development. Registration and protocol CRD42023441119.

Unsupervised Monocular Depth Estimation With Channel and Spatial Attention.

Understanding 3-D scene geometry from videos is a fundamental topic in visual perception. In this article, we propose an unsupervised monocular depth and camera motion estimation framework using unlabeled monocular videos to overcome the limitation of acquiring per-pixel ground-truth depth at scale. The photometric loss couples the depth network and pose network together and is essential to the unsupervised method, which is based on warping nearby views to target using the estimated depth and pose. We introduce the channelwise attention mechanism to dig into the relationship between channels and introduce the spatialwise attention mechanism to utilize the inner-spatial relationship of features. Both of them applied in depth networks can better activate the feature information between different convolutional layers and extract more discriminative features. In addition, we apply the Sobel boundary to our edge-aware smoothness for more reasonable accuracy, and clearer boundaries and structures. All of these help to close the gap with fully supervised methods and show high-quality state-of-the-art results on the KITTI benchmark and great generalization performance on the Make3D dataset.